Show of Hands:Votes: Party classification:: Q109244Yes_AllX_fit.models_1
bdanalytics
Date: (Mon) Jun 13, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
debugSource("~/Dropbox/datascience/R/mydsutils.R") else
source("~/Dropbox/datascience/R/mydsutils.R") ## Loading required package: caret## Loading required package: lattice# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- #NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
# '(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))' # No
# '(glbObsAll[, "Q109244"] != "")' # NA
# '(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "Yes"))' # Yes
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
#
# chk ref value against frequencies vs. alpha sort order
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q115611.fctr" # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
# retVal <- rep_len(0, length(raw))
stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0)
# msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
# msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
# msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
# msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
# msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
# msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
# msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
# msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
retVal <- sapply(raw, function(age) {
if (is.na(age)) return(0) else
if ((age > 15) && (age <= 20)) return(age - 15) else
if ((age > 20) && (age <= 25)) return(age - 20) else
if ((age > 25) && (age <= 30)) return(age - 25) else
if ((age > 30) && (age <= 35)) return(age - 30) else
if ((age > 35) && (age <= 40)) return(age - 35) else
if ((age > 40) && (age <= 50)) return(age - 40) else
if ((age > 50) && (age <= 65)) return(age - 50) else
if ((age > 65) && (age <= 90)) return(age - 65)
})
return(retVal)
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr",
# # "Hhold.fctr",
# "Edn.fctr",
# paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[",
# toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
# "]\\.[PT]\\."),
# names(glbObsAll), value = TRUE)
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(4, 6, 7, 8, 9, 10, 16, 32, 64, 128, 253) # accuracy(8) = 0.5648
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164
glbRFEResults <- NULL
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
# RFE = "Recursive Feature Elimination"
# Csm = CuStoM
# NOr = No OutlieRs
# Inc = INteraCt
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet")
} else {
# glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
glbMdlFamilies[["All.X"]] <- c("glmnet")
# glbMdlFamilies[["All.X"]] <- setdiff(glbMdlMethods, c(NULL
# # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
# , "lda" # error: model fit failed for Fold1.Rep1: parameter=none Error in lda.default(x, grouping, ...)
# ,"lda2" # error: There were missing values in resampled performance measures.
# , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
# , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
# , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
# , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
# ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
# ))
# glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet")
# glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
# # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
# # , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
# , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
# , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
# , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
# , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
# ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
# ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
# AllX__rcv_glmnetTuneParams <- rbind(data.frame() # alpha shd be <= 1.0 ALWAYS
# ,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(parameter = "lambda", vals = "0.0053781495 0.01 0.0249631588 0.03 0.04454817")
# ) # max.Accuracy.OOB = 0.5981941 @ 0.775 0.02496316
# AllX_YeoJohnson_rcv_glmnetTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(parameter = "lambda", vals = "0.0053781495 0.01 0.0249631588 0.03 0.04454817")
# ) # max.Accuracy.OOB = 0.6004515 @ 0.775 0.02496316
# AllX_zvpca_rcv_glmnetTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "alpha", vals = "0.325 0.550 0.775 0.9 1.000")
# ,data.frame(parameter = "lambda", vals = "1.040899e-03 0.003 4.831424e-03 0.01 2.242548e-02")
# ) # max.Accuracy.OOB = 0.6185102 @ 1.0 0.004831424
# # 0.616408 @ 0.9 0.004831424
#
# glbMdlTuneParams <- rbind(glbMdlTuneParams
# ,cbind(data.frame(mdlId = "All.X##rcv#glmnet"), AllX__rcv_glmnetTuneParams)
# ,cbind(data.frame(mdlId = "All.X#YeoJohnson#rcv#glmnet"),
# AllX_YeoJohnson_rcv_glmnetTuneParams) ,cbind(data.frame(mdlId = "All.X#zv.pca#rcv#glmnet"),
# AllX_zvpca_rcv_glmnetTuneParams)
# )
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# bagEarthTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "degree", vals = "1")
# ,data.frame(parameter = "nprune", vals = "256")
# )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
# bagEarthTuneParams))
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
pkgPreprocMethods <-
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
# Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
c(NULL
,"zv", "nzv"
,"BoxCox", "YeoJohnson", "expoTrans"
,"center", "scale", "center.scale", "range"
,"knnImpute", "bagImpute", "medianImpute"
,"zv.pca", "ica", "spatialSign"
,"conditionalX")
glbMdlPreprocMethods <- list(NULL # NULL # : default
# ,"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# # c(NULL)))
# c("zv.YeoJohnson.pca")))
# ,"RFE.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# c(NULL)))
# # c("zv.pca.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
# "nzv.pca.spatialSign"))
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
"max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
"min.elapsedtime.everything",
# "min.aic.fit",
"max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- "auto" # NULL : default #"auto"
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
glbMdlEnsembleSampleMethods <- c("boot", "boot632", "cv", "repeatedcv"
# , "LOOCV" # tuneLength * nrow(fitDF) # way too many models
, "LGOCV"
, "adaptive_cv" # crashed for Q109244No
# , "adaptive_boot" #error: adaptive$min should be less than 3
# , "adaptive_LGOCV" #error: adaptive$min should be less than 3
)
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- # NULL #: default
c("Q109244NA_Ensemble_cnk03_rest_out_fin.csv")
# c("Q109244No_AllXpreProc_cnk03_rest_out_fin.csv")
# c("Votes_Ensemble_cnk06_out_fin.csv")
glbOut <- list(pfx = "Q109244Yes_AllX_fit.models_1_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- NULL # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- "fit.models_1" # default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- NULL #"data/Q109244NA_Ensemble_Prep_fit.models_1_fit.models_1.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 6.392 NA NAStep 1.0: import data
chunk option: eval=
## [1] "Reading file ./data/train2016.csv..."
## [1] "dimensions of data in ./data/train2016.csv: 5,568 rows x 108 cols"
## USER_ID YOB Gender Income HouseholdStatus
## 1 1 1938 Male Married (w/kids)
## 2 4 1970 Female over $150,000 Domestic Partners (w/kids)
## 3 5 1997 Male $75,000 - $100,000 Single (no kids)
## 4 8 1983 Male $100,001 - $150,000 Married (w/kids)
## 5 9 1984 Female $50,000 - $74,999 Married (w/kids)
## 6 10 1997 Female over $150,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621 Q122769
## 1 Democrat No No No No
## 2 Bachelor's Degree Democrat Yes No No No
## 3 High School Diploma Republican Yes Yes No
## 4 Bachelor's Degree Democrat No Yes No Yes No
## 5 High School Diploma Republican No Yes No No No
## 6 Current K-12 Democrat No
## Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1 Yes Public No Yes No No No Yes
## 2 Yes Public No Yes No Yes No No Yes
## 3 Yes Private No No No Yes No No Yes
## 4 No Public No Yes No Yes No No Yes
## 5 Yes Public No Yes No Yes Yes No Yes
## 6 Yes Public No No No Yes No Yes Yes
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1 Try first No No Yes Yes
## 2 Science Study first Yes Yes No No Receiving No
## 3 Science Study first Yes No Yes Receiving No
## 4 Science Try first No Yes Yes No Giving Yes
## 5 Art Try first Yes No No No Giving No
## 6 Science Try first Yes Yes No Yes Receiving No
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797
## 1 Yes Idealist No No Yes
## 2 No Pragmatist No No Cool headed Standard hours No
## 3 Yes Pragmatist No Yes Cool headed Odd hours No
## 4 No Idealist No No Cool headed Standard hours No
## 5 No Idealist Yes Yes Hot headed Standard hours No
## 6 No Pragmatist No No Standard hours
## Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1 Happy Yes Yes No No P.M. Yes Start Yes
## 2 Happy Yes Yes Yes No A.M. No End Yes
## 3 Right Yes No No Yes A.M. Yes Start Yes
## 4 Happy Yes Yes No No A.M. Yes Start Yes
## 5 Happy Yes Yes No Yes P.M. No End No
## 6
## Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1 No Circumstances Yes Yes Yes Yes No
## 2 No Me Yes Yes No Yes No Mysterious
## 3 Yes Circumstances No Yes No Yes Yes Mysterious
## 4 No Circumstances Yes No No Yes No TMI
## 5 No Me No Yes Yes Yes Yes TMI
## 6
## Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1 Yes Yes Talk Technology No No Yes
## 2 No No
## 3 No No Tunes Technology Yes Yes Yes Yes
## 4 No No Talk People No Yes Yes Yes
## 5 Yes No Tunes People No No Yes No
## 6
## Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1 No Demanding No No Cautious No Yes!
## 2 Mac Yes Cautious No Umm...
## 3 No Supportive No PC No Cautious No Umm...
## 4 Yes Supportive No Mac Yes Risk-friendly No Umm...
## 5 No Demanding Yes PC Yes Cautious No Yes!
## 6 Yes Supportive No PC
## Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1 No Space No In-person Yes No Yes
## 2 No Space Yes In-person No Yes Yes No
## 3 No Space No In-person No No Yes Yes
## 4 No Socialize Yes Online No Yes No Yes
## 5 No Socialize No Online No No Yes Yes
## 6 In-person No No Yes Yes
## Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1 Yay people! Yes No Yes Yes No Yes
## 2 Yay people! Yes Yes Yes Yes Yes No Yes
## 3 Grrr people Yes No No No No No No
## 4 Grrr people No No Yes Yes No Yes Yes
## 5 Yay people! Yes No Yes Yes Yes Yes No
## 6 Grrr people Yes No Yes Yes No No Yes
## Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1 No No No Yes No Own Optimist
## 2
## 3 Yes No No Yes No Own Pessimist Mom
## 4 No No No Yes Yes Own Optimist Mom
## 5 No No Yes No No Own Optimist Mom
## 6 Yes Yes No Yes
## Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1 Yes Yes No No Nope Yes No No
## 2 No
## 3 No No No No Nope Yes No No No
## 4 No No No Yes Check! No No No Yes
## 5 No Yes Yes Yes Nope Yes No No Yes
## 6
## Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1 No Only-child No No Yes
## 2 No No Only-child Yes No No
## 3 Yes No Yes No Yes No
## 4 Yes No Yes No No Yes
## 5 No No Yes No No Yes
## 6
## USER_ID YOB Gender Income HouseholdStatus
## 193 245 1964 Male over $150,000 Married (w/kids)
## 848 1046 1953 Male $100,001 - $150,000 Domestic Partners (no kids)
## 2836 3530 1995 Male Single (no kids)
## 4052 5050 1945 Female $75,000 - $100,000 Married (w/kids)
## 4093 5107 1980 Female $100,001 - $150,000 Married (w/kids)
## 5509 6888 1998 Female under $25,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621
## 193 Bachelor's Degree Republican Yes Yes No Yes
## 848 Democrat
## 2836 Current Undergraduate Democrat Yes Yes Yes No
## 4052 Bachelor's Degree Republican
## 4093 Bachelor's Degree Democrat No No
## 5509 Current K-12 Republican
## Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 193 No Yes Public No Yes No Yes No
## 848
## 2836 Yes Public Yes No No Yes Yes
## 4052 No Public
## 4093 No No Private No
## 5509 Yes Yes
## Q120379 Q120650 Q120472 Q120194 Q120012 Q120014 Q119334 Q119851
## 193 No Yes Science Try first Yes Yes Yes No
## 848
## 2836 Yes Yes Art Study first No Yes Yes
## 4052
## 4093 Yes
## 5509 Yes No Art Study first Yes No Yes No
## Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186
## 193 Giving Yes No Idealist Yes Yes Hot headed
## 848
## 2836 Yes Yes Idealist Yes No Cool headed
## 4052 No No No
## 4093 No No Pragmatist No Yes
## 5509 Giving No
## Q117193 Q116797 Q116881 Q116953 Q116601 Q116441 Q116448
## 193 Standard hours No Happy Yes Yes No No
## 848
## 2836 Odd hours No Happy Yes Yes No
## 4052
## 4093
## 5509
## Q116197 Q115602 Q115777 Q115610 Q115611 Q115899 Q115390 Q114961
## 193 A.M. Yes End Yes Yes Me No No
## 848
## 2836 Yes End Yes No Circumstances Yes No
## 4052 P.M. Yes Start Yes No No
## 4093 P.M. Yes Start Yes No Circumstances
## 5509
## Q114748 Q115195 Q114517 Q114386 Q113992 Q114152 Q113583 Q113584
## 193 Yes No Yes TMI No Yes Tunes Technology
## 848
## 2836 Yes No No Mysterious No Yes Tunes People
## 4052 No Yes
## 4093 Tunes People
## 5509
## Q113181 Q112478 Q112512 Q112270 Q111848 Q111580 Q111220 Q110740
## 193 No Yes Yes Yes Supportive No Mac
## 848
## 2836 Yes Yes Yes No Yes Demanding Yes PC
## 4052
## 4093 Yes Supportive
## 5509
## Q109367 Q108950 Q109244 Q108855 Q108617 Q108856 Q108754
## 193 No Cautious No Yes! No Socialize No
## 848 Yes Risk-friendly Yes Yes! No Space No
## 2836 Yes Cautious Yes Yes
## 4052
## 4093 No Risk-friendly No Yes! No Space No
## 5509
## Q108342 Q108343 Q107869 Q107491 Q106993 Q106997 Q106272 Q106388
## 193 In-person No Yes Yes No Yay people! Yes Yes
## 848 In-person Yes
## 2836 In-person Yes Yes Yes No
## 4052 No Grrr people
## 4093 In-person Yes Yes Yes Yes Yay people! Yes Yes
## 5509
## Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906 Q102674
## 193 No Yes No No Yes No No No
## 848
## 2836 Yes No No No Yes Yes No No
## 4052 No No No No
## 4093 No No No No Yes No No Yes
## 5509
## Q102687 Q102289 Q102089 Q101162 Q101163 Q101596 Q100689 Q100680
## 193 No No Own Optimist Dad Yes Yes No
## 848
## 2836 Yes Yes Rent Optimist Dad No Yes Yes
## 4052 Yes Own No
## 4093 Yes Yes Rent No Yes
## 5509
## Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 193 Yes Check! No No No Yes Yes No Yes
## 848
## 2836 Yes Check! No No No Yes Yes Yes
## 4052
## 4093 No Nope Yes No Yes Yes Yes No Yes
## 5509
## Q98078 Q98197 Q96024
## 193 No Yes Yes
## 848 No
## 2836 Yes Yes No
## 4052
## 4093 Yes Yes No
## 5509
## USER_ID YOB Gender Income HouseholdStatus
## 5563 6955 1966 Male over $150,000 Married (w/kids)
## 5564 6956 NA Male
## 5565 6957 2000 Female
## 5566 6958 1969 Male over $150,000
## 5567 6959 1986 Male $25,001 - $50,000 Married (w/kids)
## 5568 6960 1999 Male under $25,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621
## 5563 Bachelor's Degree Democrat
## 5564 Master's Degree Democrat No No
## 5565 Current K-12 Republican
## 5566 Bachelor's Degree Democrat Yes
## 5567 High School Diploma Republican
## 5568 Current K-12 Republican
## Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 5563 No Yes No Yes Yes
## 5564 No Yes Public Yes
## 5565 Public Yes
## 5566 No No No Yes Yes
## 5567 Yes Yes No
## 5568 Yes No No
## Q120379 Q120650 Q120472 Q120194 Q120012 Q120014 Q119334 Q119851
## 5563
## 5564
## 5565 Yes Yes Art Try first No Yes Yes Yes
## 5566 Yes Yes Science
## 5567 No No Science No Yes
## 5568
## Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186 Q117193
## 5563
## 5564
## 5565 Receiving
## 5566
## 5567
## 5568
## Q116797 Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q115777 Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q114517 Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q112512 Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## 'data.frame': 5568 obs. of 20 variables:
## $ USER_ID : int 1 4 5 8 9 10 11 12 13 15 ...
## $ YOB : int 1938 1970 1997 1983 1984 1997 1983 1996 NA 1981 ...
## $ Gender : chr "Male" "Female" "Male" "Male" ...
## $ Income : chr "" "over $150,000" "$75,000 - $100,000" "$100,001 - $150,000" ...
## $ HouseholdStatus: chr "Married (w/kids)" "Domestic Partners (w/kids)" "Single (no kids)" "Married (w/kids)" ...
## $ EducationLevel : chr "" "Bachelor's Degree" "High School Diploma" "Bachelor's Degree" ...
## $ Party : chr "Democrat" "Democrat" "Republican" "Democrat" ...
## $ Q124742 : chr "No" "" "" "No" ...
## $ Q124122 : chr "" "Yes" "Yes" "Yes" ...
## $ Q123464 : chr "No" "No" "Yes" "No" ...
## $ Q123621 : chr "No" "No" "No" "Yes" ...
## $ Q122769 : chr "No" "No" "" "No" ...
## $ Q122770 : chr "Yes" "Yes" "Yes" "No" ...
## $ Q122771 : chr "Public" "Public" "Private" "Public" ...
## $ Q122120 : chr "No" "No" "No" "No" ...
## $ Q121699 : chr "Yes" "Yes" "No" "Yes" ...
## $ Q121700 : chr "No" "No" "No" "No" ...
## $ Q120978 : chr "" "Yes" "Yes" "Yes" ...
## $ Q121011 : chr "No" "No" "No" "No" ...
## $ Q120379 : chr "No" "No" "No" "No" ...
## NULL
## 'data.frame': 5568 obs. of 20 variables:
## $ Q120650: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q118117: chr "Yes" "No" "Yes" "No" ...
## $ Q118233: chr "No" "No" "No" "No" ...
## $ Q118237: chr "No" "No" "Yes" "No" ...
## $ Q116441: chr "No" "Yes" "No" "No" ...
## $ Q116197: chr "P.M." "A.M." "A.M." "A.M." ...
## $ Q115611: chr "No" "No" "Yes" "No" ...
## $ Q115899: chr "Circumstances" "Me" "Circumstances" "Circumstances" ...
## $ Q115390: chr "Yes" "Yes" "No" "Yes" ...
## $ Q114748: chr "Yes" "No" "No" "No" ...
## $ Q115195: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q113584: chr "Technology" "" "Technology" "People" ...
## $ Q112478: chr "No" "" "Yes" "Yes" ...
## $ Q112270: chr "" "" "Yes" "Yes" ...
## $ Q111848: chr "No" "" "No" "Yes" ...
## $ Q106993: chr "Yes" "No" "Yes" "Yes" ...
## $ Q106388: chr "No" "Yes" "No" "No" ...
## $ Q105655: chr "No" "No" "No" "Yes" ...
## $ Q104996: chr "Yes" "Yes" "No" "Yes" ...
## $ Q102674: chr "No" "" "No" "No" ...
## NULL
## 'data.frame': 5568 obs. of 21 variables:
## $ Q102674: chr "No" "" "No" "No" ...
## $ Q102687: chr "Yes" "" "Yes" "Yes" ...
## $ Q102289: chr "No" "" "No" "Yes" ...
## $ Q102089: chr "Own" "" "Own" "Own" ...
## $ Q101162: chr "Optimist" "" "Pessimist" "Optimist" ...
## $ Q101163: chr "" "" "Mom" "Mom" ...
## $ Q101596: chr "Yes" "" "No" "No" ...
## $ Q100689: chr "Yes" "" "No" "No" ...
## $ Q100680: chr "No" "" "No" "No" ...
## $ Q100562: chr "No" "" "No" "Yes" ...
## $ Q99982 : chr "Nope" "" "Nope" "Check!" ...
## $ Q100010: chr "Yes" "" "Yes" "No" ...
## $ Q99716 : chr "No" "" "No" "No" ...
## $ Q99581 : chr "No" "" "No" "No" ...
## $ Q99480 : chr "" "No" "No" "Yes" ...
## $ Q98869 : chr "No" "No" "Yes" "Yes" ...
## $ Q98578 : chr "" "No" "No" "No" ...
## $ Q98059 : chr "Only-child" "Only-child" "Yes" "Yes" ...
## $ Q98078 : chr "No" "Yes" "No" "No" ...
## $ Q98197 : chr "No" "No" "Yes" "No" ...
## $ Q96024 : chr "Yes" "No" "No" "Yes" ...
## NULL## Warning in myprint_str_df(obsDf): [list output truncated]## [1] "Reading file ./data/test2016.csv..."
## [1] "dimensions of data in ./data/test2016.csv: 1,392 rows x 107 cols"
## USER_ID YOB Gender Income HouseholdStatus
## 1 2 1985 Female $25,001 - $50,000 Single (no kids)
## 2 3 1983 Male $50,000 - $74,999 Married (w/kids)
## 3 6 1995 Male $75,000 - $100,000 Single (no kids)
## 4 7 1980 Female $50,000 - $74,999 Single (no kids)
## 5 14 1980 Female Married (no kids)
## 6 28 1973 Male over $150,000 Married (no kids)
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1 Master's Degree Yes No Yes No No
## 2 Current Undergraduate No Yes Yes
## 3 Current K-12
## 4 Master's Degree Yes Yes No Yes Yes Yes
## 5 Current Undergraduate Yes No Yes No No
## 6 Master's Degree No Yes No Yes No No
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650 Q120472
## 1 Public No Yes Yes Yes No Yes Yes Science
## 2 Public No Yes No
## 3 No No No Yes No Yes Science
## 4 Public No Yes No Yes No Yes Yes Science
## 5 Public Yes Yes No Yes Yes No Yes Art
## 6 Public No Yes No Yes Yes Yes Yes Science
## Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892 Q118117
## 1 Study first Yes Yes Yes No Giving Yes No
## 2 Study first No Yes No
## 3 Try first No Yes No Yes Giving
## 4 Try first Yes No No Yes Giving Yes Yes
## 5 Try first Yes Yes Yes Yes Giving No No
## 6 Try first Yes Yes No No Giving No Yes
## Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1 Idealist No Yes Cool headed Odd hours Yes Happy
## 2
## 3
## 4 Idealist No No Cool headed Standard hours No Happy
## 5 Idealist No Yes Hot headed Standard hours Yes Happy
## 6 Pragmatist Yes No Hot headed Odd hours Yes Right
## Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610 Q115611
## 1 Yes Yes No Yes A.M. Yes End Yes No
## 2 Yes Yes P.M.
## 3 Yes
## 4 Yes No No Yes A.M. Yes Start Yes No
## 5 Yes Yes Yes No P.M. Yes End No No
## 6 Yes Yes Yes Yes P.M. End Yes Yes
## Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386 Q113992
## 1 Me No Yes No Yes Yes TMI
## 2 No Yes
## 3 Yes No Yes Yes No TMI No
## 4 Me Yes No Yes Yes Yes TMI No
## 5 Me No No No Yes No TMI No
## 6 Circumstances No Yes No Yes No TMI Yes
## Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270 Q111848
## 1 No Tunes People Yes Yes No Yes Yes
## 2 No No No Yes
## 3 No Tunes Technology Yes No Yes No
## 4 Yes Talk People No No Yes No Yes
## 5 Tunes Technology No Yes Yes Yes
## 6 No Talk Technology No Yes Yes No Yes
## Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855 Q108617
## 1 Supportive No Yes Cautious Yes Yes!
## 2 No Yes Cautious No Yes! No
## 3 No No No
## 4 Supportive No PC No Cautious Yes Yes! No
## 5 Supportive Yes Mac Yes Cautious No Yes! No
## 6 Demanding No PC Yes Cautious No Umm... No
## Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993 Q106997
## 1 Yes In-person Yes
## 2 Space No Yes Yes Yes Grrr people
## 3 Yes In-person No No Yes Yes Yay people!
## 4 Space No Online No No Yes Yes Yay people!
## 5 Space No In-person No No Yes No Grrr people
## 6 Space No In-person Yes Yes Yes Grrr people
## Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906
## 1
## 2 Yes No No Yes No Yes No No
## 3 Yes No Yes No No Yes Yes No No
## 4 No No No No No Yes Yes No No
## 5 No No No Yes Yes Yes Yes Yes No
## 6 Yes No Yes Yes No No No Yes Yes
## Q102674 Q102687 Q102289 Q102089 Q101162 Q101163 Q101596 Q100689
## 1 No
## 2 Rent Pessimist Dad
## 3 No No Yes Own Optimist Mom No No
## 4 No No No Own Optimist Dad No No
## 5 Yes No No Own Pessimist Mom No Yes
## 6 Yes Yes No Own Pessimist Mom No Yes
## Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 1 Yes Yes Yes
## 2 Yes Yes Yes
## 3 Yes Yes Nope No No No Yes Yes No Yes
## 4 Yes Yes Nope Yes No No No Yes No Yes
## 5 Yes Yes Nope Yes No No Yes No No Yes
## 6 Yes Yes Nope Yes No No Yes No No Yes
## Q98078 Q98197 Q96024
## 1
## 2 Yes No Yes
## 3 No Yes Yes
## 4 No No Yes
## 5 No No No
## 6 No No Yes
## USER_ID YOB Gender Income HouseholdStatus
## 503 2555 1956 Male over $150,000 Married (w/kids)
## 515 2616 1959 Male over $150,000 Married (w/kids)
## 857 4346 1990 Female $50,000 - $74,999
## 950 4814 1969 Male $75,000 - $100,000 Married (w/kids)
## 1207 6057 1937 Female $25,001 - $50,000 Married (no kids)
## 1255 6285 1976 Female $100,001 - $150,000 Married (no kids)
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 503 Bachelor's Degree No No No Yes No Yes
## 515 Bachelor's Degree
## 857 Bachelor's Degree
## 950 Bachelor's Degree Yes No Yes No No
## 1207 Bachelor's Degree No Yes
## 1255 Bachelor's Degree
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 503 Private No Yes No No Yes No Yes
## 515 No No
## 857 No Yes No No No No Yes
## 950 Public Yes Yes No Yes Yes No Yes
## 1207 Public No Yes No No No No
## 1255
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 503 Science Study first No Yes No Yes Giving Yes
## 515 Yes
## 857 Science Study first No No Yes No Receiving Yes
## 950 Science Study first No No No No Giving No
## 1207 Study first No No Yes Receiving Yes
## 1255
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797
## 503 No Pragmatist No No Cool headed Standard hours No
## 515 No Pragmatist No Yes Cool headed Standard hours No
## 857 Yes Pragmatist No No Cool headed Odd hours No
## 950 No Pragmatist No Yes Hot headed Odd hours Yes
## 1207 No Pragmatist No No Hot headed No
## 1255
## Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777
## 503 Happy Yes Yes No No A.M. Yes End
## 515 Right Yes Yes No Yes Yes
## 857 Right Yes Yes No No A.M. Yes Start
## 950 Happy Yes Yes Yes No P.M. Yes Start
## 1207 Happy Yes Yes No No A.M. Yes Start
## 1255 Yes No Yes A.M. Yes Start
## Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517
## 503 Yes Yes Me No No No Yes Yes
## 515 Yes No Me Yes No Yes Yes No
## 857 Yes No Me No No No Yes
## 950 Yes No Me Yes No Yes No No
## 1207 No No Circumstances Yes No Yes No Yes
## 1255 Yes No Circumstances No Yes No Yes Yes
## Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512
## 503 TMI Yes Yes Tunes People Yes No Yes
## 515 No Yes Talk Technology
## 857 Mysterious No No Tunes People No No No
## 950 Mysterious No No Tunes People Yes Yes Yes
## 1207 Yes No Talk Yes
## 1255 TMI Yes Yes Yes
## Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 503 No Yes Demanding No PC No Cautious
## 515 No Yes No Mac Yes
## 857 Yes Yes Supportive No Mac No Risk-friendly
## 950 No Yes Supportive Yes PC No Cautious
## 1207 Supportive No PC Cautious
## 1255 Yes Yes Demanding No Mac
## Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 503 No Umm... No Space No In-person No Yes
## 515
## 857 Yes Umm... No Space No In-person No Yes
## 950 No Yes! No Space No In-person No No
## 1207 Yes! No Space No In-person No Yes
## 1255
## Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 503 Yes Yes Yay people! Yes No No Yes No
## 515 No
## 857 No Yes Grrr people Yes No Yes No No
## 950 Yes No Grrr people Yes Yes No No No
## 1207 Yes Yes Yes
## 1255
## Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 503 No Yes No No No Yes No Own
## 515 Yes Yes
## 857 No Yes Yes No No Yes Yes Own
## 950 Yes Yes Yes No No Yes No Own
## 1207 Yes
## 1255
## Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010
## 503 Pessimist Mom Yes Yes No Yes Check! Yes
## 515 Check! Yes
## 857 Optimist Mom No Yes Yes No Nope Yes
## 950 Pessimist Mom Yes No No No Check! Yes
## 1207
## 1255
## Q99716 Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 503 No No Yes Yes No Yes Yes Yes Yes
## 515 No Yes Yes Yes No Yes Yes
## 857 No Yes Yes Yes No Yes No No No
## 950 No No Yes Yes No Yes No Yes Yes
## 1207
## 1255
## USER_ID YOB Gender Income HouseholdStatus
## 1387 6922 1988 Male $50,000 - $74,999 Single (no kids)
## 1388 6928 1977 Female $50,000 - $74,999 Domestic Partners (no kids)
## 1389 6930 1998 Female $100,001 - $150,000 Single (no kids)
## 1390 6941 1989 Male $25,001 - $50,000 Married (no kids)
## 1391 6946 1996 Male
## 1392 6947 NA Female
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1387 Master's Degree
## 1388 Master's Degree
## 1389 Current K-12 No No
## 1390 Bachelor's Degree
## 1391 Current K-12
## 1392 Yes Yes No No No No
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1387 Yes Yes Yes Yes Yes Yes
## 1388 Yes No Yes
## 1389 Public Yes Yes Yes Yes Yes Yes Yes
## 1390 Yes Yes No No No
## 1391 Yes No No Yes No Yes Yes
## 1392 Public Yes Yes No Yes Yes Yes Yes
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1387 Science Try first No Yes Yes No Giving
## 1388 Art
## 1389 Art Study first Yes No Yes No Giving
## 1390
## 1391 Art Study first Yes Yes Yes No Giving
## 1392 Art No No No Yes Giving
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## 'data.frame': 1392 obs. of 20 variables:
## $ USER_ID : int 2 3 6 7 14 28 29 37 44 56 ...
## $ YOB : int 1985 1983 1995 1980 1980 1973 1968 1961 1989 1975 ...
## $ Gender : chr "Female" "Male" "Male" "Female" ...
## $ Income : chr "$25,001 - $50,000" "$50,000 - $74,999" "$75,000 - $100,000" "$50,000 - $74,999" ...
## $ HouseholdStatus: chr "Single (no kids)" "Married (w/kids)" "Single (no kids)" "Single (no kids)" ...
## $ EducationLevel : chr "Master's Degree" "Current Undergraduate" "Current K-12" "Master's Degree" ...
## $ Q124742 : chr "" "" "" "Yes" ...
## $ Q124122 : chr "Yes" "" "" "Yes" ...
## $ Q123464 : chr "No" "No" "" "No" ...
## $ Q123621 : chr "Yes" "" "" "Yes" ...
## $ Q122769 : chr "No" "Yes" "" "Yes" ...
## $ Q122770 : chr "No" "Yes" "" "Yes" ...
## $ Q122771 : chr "Public" "Public" "" "Public" ...
## $ Q122120 : chr "No" "No" "" "No" ...
## $ Q121699 : chr "Yes" "Yes" "No" "Yes" ...
## $ Q121700 : chr "Yes" "No" "No" "No" ...
## $ Q120978 : chr "Yes" "" "No" "Yes" ...
## $ Q121011 : chr "No" "" "Yes" "No" ...
## $ Q120379 : chr "Yes" "" "No" "Yes" ...
## $ Q120650 : chr "Yes" "" "Yes" "Yes" ...
## NULL
## 'data.frame': 1392 obs. of 20 variables:
## $ Q120012: chr "Yes" "No" "No" "Yes" ...
## $ Q120014: chr "Yes" "Yes" "Yes" "No" ...
## $ Q118117: chr "No" "" "" "Yes" ...
## $ Q118237: chr "Yes" "" "" "No" ...
## $ Q116953: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q116601: chr "Yes" "Yes" "" "No" ...
## $ Q116448: chr "Yes" "" "" "Yes" ...
## $ Q116197: chr "A.M." "P.M." "" "A.M." ...
## $ Q115899: chr "Me" "" "" "Me" ...
## $ Q114961: chr "Yes" "" "No" "No" ...
## $ Q113584: chr "People" "" "Technology" "People" ...
## $ Q113181: chr "Yes" "No" "Yes" "No" ...
## $ Q112512: chr "No" "" "Yes" "Yes" ...
## $ Q108950: chr "Cautious" "Cautious" "" "Cautious" ...
## $ Q108617: chr "" "No" "No" "No" ...
## $ Q108342: chr "In-person" "" "In-person" "Online" ...
## $ Q107491: chr "" "Yes" "Yes" "Yes" ...
## $ Q106272: chr "" "Yes" "Yes" "No" ...
## $ Q106389: chr "" "No" "Yes" "No" ...
## $ Q104996: chr "" "No" "Yes" "Yes" ...
## NULL
## 'data.frame': 1392 obs. of 21 variables:
## $ Q102674: chr "" "" "No" "No" ...
## $ Q102687: chr "" "" "No" "No" ...
## $ Q102289: chr "" "" "Yes" "No" ...
## $ Q102089: chr "" "Rent" "Own" "Own" ...
## $ Q101162: chr "" "Pessimist" "Optimist" "Optimist" ...
## $ Q101163: chr "" "Dad" "Mom" "Dad" ...
## $ Q101596: chr "" "" "No" "No" ...
## $ Q100689: chr "No" "" "No" "No" ...
## $ Q100680: chr "Yes" "" "Yes" "Yes" ...
## $ Q100562: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q99982 : chr "" "" "Nope" "Nope" ...
## $ Q100010: chr "" "" "No" "Yes" ...
## $ Q99716 : chr "" "" "No" "No" ...
## $ Q99581 : chr "" "" "No" "No" ...
## $ Q99480 : chr "" "" "Yes" "No" ...
## $ Q98869 : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q98578 : chr "" "" "No" "No" ...
## $ Q98059 : chr "" "Yes" "Yes" "Yes" ...
## $ Q98078 : chr "" "Yes" "No" "No" ...
## $ Q98197 : chr "" "No" "Yes" "No" ...
## $ Q96024 : chr "" "Yes" "Yes" "Yes" ...
## NULL## Warning in myprint_str_df(obsDf): [list output truncated]## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: YOB.Age.fctr..."
## [1] "Creating new feature: YOB.Age.dff..."
## [1] "Creating new feature: Gender.fctr..."
## [1] "Creating new feature: Income.fctr..."
## [1] "Creating new feature: Hhold.fctr..."
## [1] "Creating new feature: Edn.fctr..."
## [1] "Creating new feature: Q124742.fctr..."
## [1] "Creating new feature: Q124122.fctr..."
## [1] "Creating new feature: Q123621.fctr..."
## [1] "Creating new feature: Q123464.fctr..."
## [1] "Creating new feature: Q122771.fctr..."
## [1] "Creating new feature: Q122770.fctr..."
## [1] "Creating new feature: Q122769.fctr..."
## [1] "Creating new feature: Q122120.fctr..."
## [1] "Creating new feature: Q121700.fctr..."
## [1] "Creating new feature: Q121699.fctr..."
## [1] "Creating new feature: Q121011.fctr..."
## [1] "Creating new feature: Q120978.fctr..."
## [1] "Creating new feature: Q120650.fctr..."
## [1] "Creating new feature: Q120472.fctr..."
## [1] "Creating new feature: Q120379.fctr..."
## [1] "Creating new feature: Q120194.fctr..."
## [1] "Creating new feature: Q120014.fctr..."
## [1] "Creating new feature: Q120012.fctr..."
## [1] "Creating new feature: Q119851.fctr..."
## [1] "Creating new feature: Q119650.fctr..."
## [1] "Creating new feature: Q119334.fctr..."
## [1] "Creating new feature: Q118892.fctr..."
## [1] "Creating new feature: Q118237.fctr..."
## [1] "Creating new feature: Q118233.fctr..."
## [1] "Creating new feature: Q118232.fctr..."
## [1] "Creating new feature: Q118117.fctr..."
## [1] "Creating new feature: Q117193.fctr..."
## [1] "Creating new feature: Q117186.fctr..."
## [1] "Creating new feature: Q116797.fctr..."
## [1] "Creating new feature: Q116881.fctr..."
## [1] "Creating new feature: Q116953.fctr..."
## [1] "Creating new feature: Q116601.fctr..."
## [1] "Creating new feature: Q116441.fctr..."
## [1] "Creating new feature: Q116448.fctr..."
## [1] "Creating new feature: Q116197.fctr..."
## [1] "Creating new feature: Q115602.fctr..."
## [1] "Creating new feature: Q115777.fctr..."
## [1] "Creating new feature: Q115610.fctr..."
## [1] "Creating new feature: Q115611.fctr..."
## [1] "Creating new feature: Q115899.fctr..."
## [1] "Creating new feature: Q115390.fctr..."
## [1] "Creating new feature: Q115195.fctr..."
## [1] "Creating new feature: Q114961.fctr..."
## [1] "Creating new feature: Q114748.fctr..."
## [1] "Creating new feature: Q114517.fctr..."
## [1] "Creating new feature: Q114386.fctr..."
## [1] "Creating new feature: Q114152.fctr..."
## [1] "Creating new feature: Q113992.fctr..."
## [1] "Creating new feature: Q113583.fctr..."
## [1] "Creating new feature: Q113584.fctr..."
## [1] "Creating new feature: Q113181.fctr..."
## [1] "Creating new feature: Q112478.fctr..."
## [1] "Creating new feature: Q112512.fctr..."
## [1] "Creating new feature: Q112270.fctr..."
## [1] "Creating new feature: Q111848.fctr..."
## [1] "Creating new feature: Q111580.fctr..."
## [1] "Creating new feature: Q111220.fctr..."
## [1] "Creating new feature: Q110740.fctr..."
## [1] "Creating new feature: Q109367.fctr..."
## [1] "Creating new feature: Q109244.fctr..."
## [1] "Creating new feature: Q108950.fctr..."
## [1] "Creating new feature: Q108855.fctr..."
## [1] "Creating new feature: Q108617.fctr..."
## [1] "Creating new feature: Q108856.fctr..."
## [1] "Creating new feature: Q108754.fctr..."
## [1] "Creating new feature: Q108342.fctr..."
## [1] "Creating new feature: Q108343.fctr..."
## [1] "Creating new feature: Q107869.fctr..."
## [1] "Creating new feature: Q107491.fctr..."
## [1] "Creating new feature: Q106993.fctr..."
## [1] "Creating new feature: Q106997.fctr..."
## [1] "Creating new feature: Q106272.fctr..."
## [1] "Creating new feature: Q106388.fctr..."
## [1] "Creating new feature: Q106389.fctr..."
## [1] "Creating new feature: Q106042.fctr..."
## [1] "Creating new feature: Q105840.fctr..."
## [1] "Creating new feature: Q105655.fctr..."
## [1] "Creating new feature: Q104996.fctr..."
## [1] "Creating new feature: Q103293.fctr..."
## [1] "Creating new feature: Q102906.fctr..."
## [1] "Creating new feature: Q102674.fctr..."
## [1] "Creating new feature: Q102687.fctr..."
## [1] "Creating new feature: Q102289.fctr..."
## [1] "Creating new feature: Q102089.fctr..."
## [1] "Creating new feature: Q101162.fctr..."
## [1] "Creating new feature: Q101163.fctr..."
## [1] "Creating new feature: Q101596.fctr..."
## [1] "Creating new feature: Q100689.fctr..."
## [1] "Creating new feature: Q100680.fctr..."
## [1] "Creating new feature: Q100562.fctr..."
## [1] "Creating new feature: Q100010.fctr..."
## [1] "Creating new feature: Q99982.fctr..."
## [1] "Creating new feature: Q99716.fctr..."
## [1] "Creating new feature: Q99581.fctr..."
## [1] "Creating new feature: Q99480.fctr..."
## [1] "Creating new feature: Q98869.fctr..."
## [1] "Creating new feature: Q98578.fctr..."
## [1] "Creating new feature: Q98197.fctr..."
## [1] "Creating new feature: Q98059.fctr..."
## [1] "Creating new feature: Q98078.fctr..."
## [1] "Creating new feature: Q96024.fctr..."## [1] "Partition stats:"## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk## Party .src .n
## 1 Democrat Train 2951
## 2 Republican Train 2617
## 3 <NA> Test 1392
## Party .src .n
## 1 Democrat Train 2951
## 2 Republican Train 2617
## 3 <NA> Test 1392## Loading required package: RColorBrewer
## .src .n
## 1 Train 5568
## 2 Test 1392## Loading required package: lazyeval## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## [1] "Found 0 duplicates by all features:"## NULL## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 6.392 16.547 10.155
## 2 inspect.data 2 0 0 16.548 NA NAStep 2.0: inspect data
## Warning: Removed 1392 rows containing non-finite values (stat_count).## Loading required package: reshape2
## Party.Democrat Party.Republican Party.NA
## Test NA NA 1392
## Train 2951 2617 NA
## Party.Democrat Party.Republican Party.NA
## Test NA NA 1
## Train 0.5299928 0.4700072 NA
## [1] "numeric data missing in glbObsAll: "
## YOB
## 415
## [1] "numeric data w/ 0s in glbObsAll: "
## YOB.Age.dff
## 438
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## Party Party.fctr .n
## 1 Democrat D 2951
## 2 Republican R 2617
## 3 <NA> <NA> 1392## Warning: Removed 1 rows containing missing values (position_stack).
## Party.fctr.D Party.fctr.R Party.fctr.NA
## Test NA NA 1392
## Train 2951 2617 NA
## Party.fctr.D Party.fctr.R Party.fctr.NA
## Test NA NA 1
## Train 0.5299928 0.4700072 NA
## [1] "elapsed Time (secs): 8.668000"## Loading required package: caTools## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "elapsed Time (secs): 146.628000"
## [1] "elapsed Time (secs): 146.628000"## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 16.548 174.407 157.859
## 3 scrub.data 2 1 1 174.407 NA NAStep 2.1: scrub data
```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## YOB.Age.dff
## 438
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 174.407 212.405 37.998
## 4 transform.data 2 2 2 212.405 NA NAStep 2.2: transform data
## label step_major step_minor label_minor bgn end
## 4 transform.data 2 2 2 212.405 212.451
## 5 extract.features 3 0 0 212.451 NA
## elapsed
## 4 0.046
## 5 NAStep 3.0: extract features
## label step_major step_minor label_minor bgn
## 5 extract.features 3 0 0 212.451
## 6 extract.features.datetime 3 1 1 212.474
## end elapsed
## 5 212.473 0.022
## 6 NA NAStep 3.1: extract features datetime
## label step_major step_minor label_minor bgn
## 1 extract.features.datetime.bgn 1 0 0 212.503
## end elapsed
## 1 NA NA## label step_major step_minor label_minor bgn
## 6 extract.features.datetime 3 1 1 212.474
## 7 extract.features.image 3 2 2 212.517
## end elapsed
## 6 212.517 0.043
## 7 NA NAStep 3.2: extract features image
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end
## 1 extract.features.image.bgn 1 0 0 212.553 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 212.553
## 2 extract.features.image.end 2 0 0 212.563
## end elapsed
## 1 212.563 0.01
## 2 NA NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 212.553
## 2 extract.features.image.end 2 0 0 212.563
## end elapsed
## 1 212.563 0.01
## 2 NA NA## label step_major step_minor label_minor bgn end
## 7 extract.features.image 3 2 2 212.517 212.574
## 8 extract.features.price 3 3 3 212.574 NA
## elapsed
## 7 0.057
## 8 NAStep 3.3: extract features price
## label step_major step_minor label_minor bgn end
## 1 extract.features.price.bgn 1 0 0 212.602 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 8 extract.features.price 3 3 3 212.574 212.612
## 9 extract.features.text 3 4 4 212.613 NA
## elapsed
## 8 0.038
## 9 NAStep 3.4: extract features text
## label step_major step_minor label_minor bgn end
## 1 extract.features.text.bgn 1 0 0 212.659 NA
## elapsed
## 1 NA## Warning in rm(tmp_allobs_df): object 'tmp_allobs_df' not found## Warning in rm(tmp_trnobs_df): object 'tmp_trnobs_df' not found## label step_major step_minor label_minor bgn
## 9 extract.features.text 3 4 4 212.613
## 10 extract.features.string 3 5 5 212.673
## end elapsed
## 9 212.673 0.06
## 10 NA NAStep 3.5: extract features string
## label step_major step_minor label_minor bgn
## 1 extract.features.string.bgn 1 0 0 212.729
## end elapsed
## 1 NA NA## label step_major step_minor
## 1 extract.features.string.bgn 1 0
## 2 extract.features.stringfactorize.str.vars 2 0
## label_minor bgn end elapsed
## 1 0 212.729 212.738 0.01
## 2 0 212.739 NA NA## Gender Income HouseholdStatus EducationLevel
## "Gender" "Income" "HouseholdStatus" "EducationLevel"
## Party Q124742 Q124122 Q123464
## "Party" "Q124742" "Q124122" "Q123464"
## Q123621 Q122769 Q122770 Q122771
## "Q123621" "Q122769" "Q122770" "Q122771"
## Q122120 Q121699 Q121700 Q120978
## "Q122120" "Q121699" "Q121700" "Q120978"
## Q121011 Q120379 Q120650 Q120472
## "Q121011" "Q120379" "Q120650" "Q120472"
## Q120194 Q120012 Q120014 Q119334
## "Q120194" "Q120012" "Q120014" "Q119334"
## Q119851 Q119650 Q118892 Q118117
## "Q119851" "Q119650" "Q118892" "Q118117"
## Q118232 Q118233 Q118237 Q117186
## "Q118232" "Q118233" "Q118237" "Q117186"
## Q117193 Q116797 Q116881 Q116953
## "Q117193" "Q116797" "Q116881" "Q116953"
## Q116601 Q116441 Q116448 Q116197
## "Q116601" "Q116441" "Q116448" "Q116197"
## Q115602 Q115777 Q115610 Q115611
## "Q115602" "Q115777" "Q115610" "Q115611"
## Q115899 Q115390 Q114961 Q114748
## "Q115899" "Q115390" "Q114961" "Q114748"
## Q115195 Q114517 Q114386 Q113992
## "Q115195" "Q114517" "Q114386" "Q113992"
## Q114152 Q113583 Q113584 Q113181
## "Q114152" "Q113583" "Q113584" "Q113181"
## Q112478 Q112512 Q112270 Q111848
## "Q112478" "Q112512" "Q112270" "Q111848"
## Q111580 Q111220 Q110740 Q109367
## "Q111580" "Q111220" "Q110740" "Q109367"
## Q108950 Q109244 Q108855 Q108617
## "Q108950" "Q109244" "Q108855" "Q108617"
## Q108856 Q108754 Q108342 Q108343
## "Q108856" "Q108754" "Q108342" "Q108343"
## Q107869 Q107491 Q106993 Q106997
## "Q107869" "Q107491" "Q106993" "Q106997"
## Q106272 Q106388 Q106389 Q106042
## "Q106272" "Q106388" "Q106389" "Q106042"
## Q105840 Q105655 Q104996 Q103293
## "Q105840" "Q105655" "Q104996" "Q103293"
## Q102906 Q102674 Q102687 Q102289
## "Q102906" "Q102674" "Q102687" "Q102289"
## Q102089 Q101162 Q101163 Q101596
## "Q102089" "Q101162" "Q101163" "Q101596"
## Q100689 Q100680 Q100562 Q99982
## "Q100689" "Q100680" "Q100562" "Q99982"
## Q100010 Q99716 Q99581 Q99480
## "Q100010" "Q99716" "Q99581" "Q99480"
## Q98869 Q98578 Q98059 Q98078
## "Q98869" "Q98578" "Q98059" "Q98078"
## Q98197 Q96024 .src
## "Q98197" "Q96024" ".src"## label step_major step_minor label_minor bgn
## 10 extract.features.string 3 5 5 212.673
## 11 extract.features.end 3 6 6 212.762
## end elapsed
## 10 212.762 0.089
## 11 NA NAStep 3.6: extract features end
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## label step_major step_minor label_minor bgn end
## 11 extract.features.end 3 6 6 212.762 213.679
## 12 manage.missing.data 4 0 0 213.680 NA
## elapsed
## 11 0.917
## 12 NAStep 4.0: manage missing data
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## YOB.Age.dff
## 438
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## YOB.Age.dff
## 438
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024
## 2836 2858## label step_major step_minor label_minor bgn end
## 12 manage.missing.data 4 0 0 213.680 214.567
## 13 cluster.data 5 0 0 214.568 NA
## elapsed
## 12 0.887
## 13 NAStep 5.0: cluster data
```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## Loading required package: proxy##
## Attaching package: 'proxy'## The following objects are masked from 'package:stats':
##
## as.dist, dist## The following object is masked from 'package:base':
##
## as.matrix## Loading required package: dynamicTreeCut## Loading required package: entropy## Loading required package: tidyr## Loading required package: ggdendro## [1] "Clustering features: "
## abs.cor.y
## Q120472.fctr 0.04620307
## Q98197.fctr 0.05493425
## Q113181.fctr 0.08087531
## Q115611.fctr 0.09044682
## Q109244.fctr 0.12038125
## [1] " .rnorm cor: 0.0078"
## [1] " Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.6913"
## Hhold.fctr .clusterid Hhold.fctr.clusterid D R .entropy .knt
## 1 N 1 N_1 230 220 0.6929002 450
## 2 MKn 1 MKn_1 344 308 0.6916221 652
## 3 MKy 1 MKy_1 752 842 0.6915524 1594
## 4 PKn 1 PKn_1 131 49 0.5854566 180
## 5 PKy 1 PKy_1 35 26 0.6822232 61
## 6 SKn 1 SKn_1 1340 1091 0.6878923 2431
## 7 SKy 1 SKy_1 119 81 0.6749870 200
## [1] "glbObsAll$Hhold.fctr Entropy: 0.6859 (99.2186 pct)"## [1] "Category: N"
## [1] "max distance(0.9785) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4459 5563 D N NA NA NA
## 5038 6295 R N No No No
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4459 NA NA NA NA NA
## 5038 No Pc Yes Yes Yes
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4459 NA NA NA NA NA
## 5038 No No No No Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4459 NA NA NA NA NA
## 5038 Science Yes Try first Yes No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4459 Yes Giving No Yes No
## 5038 No Giving No No No
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4459 Yes Pr No Standard hours Hot headed
## 5038 NA NA Yes Standard hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4459 NA NA NA NA NA
## 5038 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4459 NA NA NA NA NA
## 5038 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4459 NA NA NA NA NA
## 5038 NA NA NA No Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4459 NA NA NA NA NA
## 5038 Yes No Mysterious Yes No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4459 NA NA NA Yes NA
## 5038 Tunes People NA No NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4459 Yes No Supportive No Mac
## 5038 No Yes NA NA Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4459 NA NA Risk-friendly Yes! No
## 5038 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4459 Space No In-person No No
## 5038 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4459 Yes Yes Gr Yes No
## 5038 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4459 Yes No No Yes No
## 5038 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4459 Yes Yes No Yes No
## 5038 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4459 Rent Optimist Mom No Yes
## 5038 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4459 No No Yes Nope No
## 5038 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4459 No No Yes No No
## 5038 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4459 Yes Yes Yes
## 5038 NA NA NA
## [1] "min distance(0.9441) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4930 6157 D N Yes NA NA
## 6569 5059 <NA> N NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4930 NA NA NA NA NA
## 6569 NA Pc No NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4930 NA NA NA NA Yes
## 6569 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4930 No No Mysterious No No
## 6569 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4930 Tunes Technology NA NA NA
## 6569 Tunes Technology No No Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4930 NA NA NA NA NA
## 6569 No NA NA No NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4930 Yes Yes Cautious NA NA
## 6569 No Yes NA Umm... Yes
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4930 NA NA NA NA NA
## 6569 Space NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4930 NA NA NA NA NA
## 6569 Yes Yes NA No NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4930 NA NA NA NA NA
## 6569 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4930 NA NA NA
## 6569 NA NA NA
## [1] "Category: MKn"
## [1] "max distance(0.9784) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3547 4420 D MKn NA NA NA
## 5337 6664 D MKn Yes Yes Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3547 NA NA NA NA NA
## 5337 No Pc Yes Yes NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3547 NA NA NA NA NA
## 5337 No Yes Yes Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3547 NA NA NA NA NA
## 5337 Science Yes Study first Yes No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3547 NA NA NA NA NA
## 5337 Yes Receiving Yes Yes NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3547 NA NA NA NA NA
## 5337 NA Pr No NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3547 NA NA NA NA NA
## 5337 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3547 NA NA NA NA NA
## 5337 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3547 NA NA Yes Yes No
## 5337 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3547 Yes No TMI No No
## 5337 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3547 Tunes People NA Yes Yes
## 5337 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3547 No Yes Demanding Yes PC
## 5337 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3547 No NA Cautious Umm... No
## 5337 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3547 Space No NA NA NA
## 5337 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3547 NA NA NA NA NA
## 5337 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3547 NA NA NA NA NA
## 5337 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3547 No No No No No
## 5337 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3547 Own Optimist Mom Yes Yes
## 5337 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3547 Yes No Yes Check! No
## 5337 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3547 No Yes Yes No No
## 5337 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 3547 Yes No Yes
## 5337 NA NA NA
## [1] "min distance(0.9373) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2019 2509 D MKn NA NA NA
## 3363 4185 D MKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2019 NA Yes Demanding No Mac
## 3363 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2019 Yes Yes Risk-friendly NA NA
## 3363 NA Yes NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2019 NA NA NA NA NA
## 3363 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 2019 NA NA NA
## 3363 NA NA NA
## [1] "Category: MKy"
## [1] "max distance(0.9789) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 6014 2335 <NA> MKy NA NA NA
## 6304 3742 <NA> MKy No Yes Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 6014 NA NA NA NA NA
## 6304 No Pc Yes Yes No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 6014 NA NA NA NA NA
## 6304 No Yes Yes No Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 6014 NA NA NA NA NA
## 6304 Science Yes Study first Yes Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 6014 NA NA NA NA NA
## 6304 No Giving No Yes No
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 6014 NA NA NA NA NA
## 6304 No Pr No Odd hours NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 6014 NA NA NA NA NA
## 6304 No Happy Yes Yes Yes
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 6014 NA P.M. NA NA NA
## 6304 No NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 6014 NA NA NA NA NA
## 6304 NA NA NA Yes NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 6014 NA NA NA NA NA
## 6304 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 6014 NA NA NA Yes Yes
## 6304 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 6014 No Yes Supportive No PC
## 6304 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 6014 NA NA Cautious Umm... No
## 6304 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 6014 Space No Online Yes No
## 6304 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 6014 Yes Yes Yy Yes No
## 6304 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 6014 No Yes No Yes No
## 6304 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 6014 Yes No No Yes No
## 6304 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 6014 Own Pessimist Dad Yes Yes
## 6304 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 6014 Yes No Yes Nope No
## 6304 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 6014 No Yes NA NA NA
## 6304 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 6014 NA NA Yes
## 6304 NA NA NA
## [1] "min distance(0.9430) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3335 4153 D MKy NA NA NA
## 5966 2056 <NA> MKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3335 NA NA NA Yes Yes
## 5966 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3335 No Yes Mysterious Yes Yes
## 5966 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3335 Talk People No NA NA
## 5966 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA PC
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3335 No Yes Risk-friendly NA NA
## 5966 Yes Yes NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3335 Space NA NA NA NA
## 5966 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3335 NA NA NA NA NA
## 5966 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 3335 NA NA NA
## 5966 NA NA NA
## [1] "Category: PKn"
## [1] "max distance(0.9770) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2165 2698 R PKn NA NA NA
## 5415 6762 D PKn Yes Yes No
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2165 NA NA NA NA NA
## 5415 No Pt No Yes Yes
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2165 NA NA NA NA NA
## 5415 Yes Yes Yes No Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2165 NA NA NA NA NA
## 5415 Science Yes Try first Yes No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2165 NA NA NA Yes No
## 5415 Yes Giving Yes Yes NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2165 No Id No Odd hours Hot headed
## 5415 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2165 Yes Happy No Yes No
## 5415 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2165 NA A.M. NA NA NA
## 5415 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2165 NA NA No Yes No
## 5415 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2165 Yes Yes TMI Yes Yes
## 5415 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2165 Talk Technology NA No Yes
## 5415 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2165 No Yes Demanding Yes Mac
## 5415 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2165 Yes No Cautious Umm... No
## 5415 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2165 Socialize No Online No Yes
## 5415 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2165 Yes Yes Yy Yes Yes
## 5415 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2165 No Yes No No No
## 5415 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2165 No No No Yes No
## 5415 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2165 Own Pessimist NA Yes Yes
## 5415 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2165 Yes No Yes Check! No
## 5415 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2165 No Yes NA Yes Yes
## 5415 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 2165 Yes No Yes
## 5415 NA NA NA
## [1] "min distance(0.9426) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 848 1046 D PKn NA NA NA
## 3463 4312 D PKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA Yes Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 848 Yes Yes Risk-friendly Yes! No
## 3463 Yes Yes Risk-friendly Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 848 Space No In-person Yes NA
## 3463 Space Yes NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 848 NA NA No
## 3463 NA NA NA
## [1] "Category: PKy"
## [1] "max distance(0.9776) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1561 1933 R PKy NA NA NA
## 2346 2921 R PKy No No Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1561 NA NA NA NA NA
## 2346 No Pc No No Yes
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1561 NA NA NA NA NA
## 2346 No Yes Yes Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1561 NA NA NA NA NA
## 2346 Science Yes Study first Yes No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1561 NA NA NA NA Yes
## 2346 Yes Giving Yes Yes No
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1561 Yes Pr No NA Cool headed
## 2346 Yes Id No Odd hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1561 No Happy No Yes No
## 2346 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1561 No A.M. Yes Start Yes
## 2346 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1561 NA Cs Yes No No
## 2346 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1561 Yes Yes Mysterious No No
## 2346 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1561 NA NA NA NA NA
## 2346 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1561 NA NA NA NA NA
## 2346 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1561 NA NA NA NA NA
## 2346 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1561 NA NA NA NA NA
## 2346 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1561 NA NA NA Yes No
## 2346 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1561 No No Yes No NA
## 2346 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1561 NA NA NA NA NA
## 2346 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1561 NA NA NA NA Yes
## 2346 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1561 Yes No Yes Check! No
## 2346 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1561 No Yes Yes No No
## 2346 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1561 Yes Yes Yes
## 2346 NA NA NA
## [1] "min distance(0.9481) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2107 2623 D PKy NA NA NA
## 6815 6244 <NA> PKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2107 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2107 NA NA Yes Yes Yes
## 6815 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2107 Art NA NA Yes Yes
## 6815 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2107 Yes NA NA NA NA
## 6815 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2107 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2107 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2107 NA NA NA NA Yes
## 6815 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2107 No NA NA Yes Yes
## 6815 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2107 No Yes TMI Yes Yes
## 6815 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2107 Talk NA NA NA NA
## 6815 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2107 NA NA NA NA PC
## 6815 NA NA NA NA PC
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2107 Yes Yes NA Umm... NA
## 6815 Yes Yes Risk-friendly Umm... No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2107 NA Yes NA No NA
## 6815 Space No NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2107 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2107 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2107 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2107 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2107 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2107 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 2107 NA NA NA
## 6815 NA NA NA
## [1] "Category: SKn"
## [1] "max distance(0.9786) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3905 4863 R SKn NA NA NA
## 4010 4997 D SKn NA NA No
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3905 NA NA NA NA NA
## 4010 No Pt Yes No NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3905 NA NA NA NA NA
## 4010 Yes Yes Yes Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3905 NA NA NA NA NA
## 4010 NA Yes NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3905 NA NA NA NA No
## 4010 NA NA Yes NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3905 Yes Id No Standard hours Cool headed
## 4010 NA Id No NA Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3905 Yes Happy Yes Yes Yes
## 4010 NA NA NA Yes NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3905 Yes P.M. NA NA NA
## 4010 No P.M. NA Start NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3905 NA NA NA NA NA
## 4010 NA NA NA Yes No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3905 NA NA NA NA NA
## 4010 No No Mysterious NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3905 NA NA NA NA NA
## 4010 Tunes Technology NA NA Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3905 NA NA NA NA NA
## 4010 Yes NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3905 NA NA NA NA NA
## 4010 NA NA NA Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3905 NA NA NA NA No
## 4010 NA No In-person NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3905 No Yes Gr Yes Yes
## 4010 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3905 Yes Yes Yes Yes NA
## 4010 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3905 No Yes Yes Yes No
## 4010 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3905 Own Pessimist Mom Yes Yes
## 4010 NA Optimist Dad NA No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3905 No No Yes Nope Yes
## 4010 No Yes NA Check! No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3905 Yes Yes No NA NA
## 4010 No NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 3905 NA NA NA
## 4010 Yes Yes Yes
## [1] "min distance(0.9380) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2524 3142 D SKn NA NA NA
## 2712 3375 D SKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2524 Yes Yes NA Umm... NA
## 2712 No Yes Cautious NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2524 NA No Online Yes NA
## 2712 NA No NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2524 NA NA NA NA NA
## 2712 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 2524 NA NA NA
## 2712 NA NA NA
## [1] "Category: SKy"
## [1] "max distance(0.9776) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2213 2759 R SKy NA NA NA
## 6921 6795 <NA> SKy Yes Yes No
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2213 NA NA NA NA NA
## 6921 No Pc Yes Yes Yes
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2213 NA NA NA NA NA
## 6921 Yes No Yes Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2213 NA NA NA NA NA
## 6921 Science Yes Try first Yes Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2213 NA NA NA NA NA
## 6921 No Giving Yes Yes NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2213 NA NA NA NA NA
## 6921 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2213 NA NA NA NA NA
## 6921 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2213 NA NA NA NA NA
## 6921 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2213 NA NA NA NA NA
## 6921 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2213 NA NA NA NA NA
## 6921 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2213 NA NA NA NA NA
## 6921 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2213 NA NA Demanding Yes PC
## 6921 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2213 NA NA NA NA NA
## 6921 NA NA NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2213 NA NA NA NA Yes
## 6921 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2213 Yes Yes Gr Yes No
## 6921 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2213 Yes Yes No No No
## 6921 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2213 Yes Yes Yes No No
## 6921 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2213 Own Pessimist Mom Yes Yes
## 6921 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2213 Yes Yes Yes Nope No
## 6921 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2213 Yes Yes Yes No No
## 6921 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 2213 Yes No No
## 6921 NA NA NA
## [1] "min distance(0.9466) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2539 3157 R SKy NA NA NA
## 3850 4797 D SKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2539 NA NA NA NA NA
## 3850 Yes No Mysterious No No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2539 NA NA NA NA NA
## 3850 Talk People NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2539 NA No NA NA NA
## 3850 NA No NA No Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2539 NA Yes NA NA NA
## 3850 NA Yes Risk-friendly Umm... Yes
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2539 NA NA NA NA NA
## 3850 Space Yes In-person No Yes
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2539 NA NA NA NA NA
## 3850 No Yes Yy No No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2539 NA NA NA NA NA
## 3850 Yes Yes Yes No No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2539 Rent NA NA NA NA
## 3850 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2539 NA NA NA NA NA
## 3850 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 2539 NA NA NA
## 3850 NA NA NA
## Hhold.fctr .clusterid Hhold.fctr.clusterid D R .entropy .knt
## 1 N 1 N_1 128 129 0.6931396 257
## 2 N 2 N_2 45 54 0.6890092 99
## 3 N 3 N_3 57 37 0.6703386 94
## 4 MKn 1 MKn_1 166 159 0.6929152 325
## 5 MKn 2 MKn_2 110 31 0.5267284 141
## 6 MKn 3 MKn_3 43 76 0.6541879 119
## 7 MKn 4 MKn_4 25 42 0.6606028 67
## 8 MKy 1 MKy_1 508 560 0.6919614 1068
## 9 MKy 2 MKy_2 126 230 0.6498471 356
## 10 MKy 3 MKy_3 118 52 0.6157663 170
## 11 PKn 1 PKn_1 30 16 0.6460905 46
## 12 PKn 2 PKn_2 43 4 0.2910671 47
## 13 PKn 3 PKn_3 26 16 0.6645284 42
## 14 PKn 4 PKn_4 32 13 0.6011538 45
## 15 PKy 1 PKy_1 14 9 0.6693280 23
## 16 PKy 2 PKy_2 8 13 0.6645284 21
## 17 PKy 3 PKy_3 13 4 0.5455946 17
## 18 SKn 1 SKn_1 492 513 0.6929289 1005
## 19 SKn 2 SKn_2 452 413 0.6921304 865
## 20 SKn 3 SKn_3 396 165 0.6057975 561
## 21 SKy 1 SKy_1 50 43 0.6903118 93
## 22 SKy 2 SKy_2 25 25 0.6931472 50
## 23 SKy 3 SKy_3 44 13 0.5369340 57
## [1] "glbObsAll$Hhold.fctr$.clusterid Entropy: 0.6658 (97.0682 pct)"## label step_major step_minor label_minor bgn
## 13 cluster.data 5 0 0 214.568
## 14 partition.data.training 6 0 0 369.043
## end elapsed
## 13 369.043 154.475
## 14 NA NAStep 6.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 0.14 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 0.14 secs"
## [1] "lclgetMatrixSimilarity: duration: 64.963000 secs"## Loading required package: sampling##
## Attaching package: 'sampling'## The following object is masked from 'package:caret':
##
## cluster## Stratum 1
##
## Population total and number of selected units: 230 44
## Stratum 2
##
## Population total and number of selected units: 344 72
## Stratum 3
##
## Population total and number of selected units: 752 158
## Stratum 4
##
## Population total and number of selected units: 131 16
## Stratum 5
##
## Population total and number of selected units: 35 5
## Stratum 6
##
## Population total and number of selected units: 1340 271
## Stratum 7
##
## Population total and number of selected units: 119 28
## Stratum 8
##
## Population total and number of selected units: 220 39
## Stratum 9
##
## Population total and number of selected units: 308 64
## Stratum 10
##
## Population total and number of selected units: 842 140
## Stratum 11
##
## Population total and number of selected units: 49 14
## Stratum 12
##
## Population total and number of selected units: 26 4
## Stratum 13
##
## Population total and number of selected units: 1091 240
## Stratum 14
##
## Population total and number of selected units: 81 25
## Number of strata 14
## Total number of selected units 1120
## [1] "lclgetMatrixSimilarity: duration: 44.169000 secs"
## [1] "lclgetMatrixSimilarity: duration: 14.249000 secs"
## [1] "lclgetMatrixSimilarity: duration: 14.143000 secs"
## [1] "lclgetMatrixSimilarity: duration: 44.777000 secs"
## [1] "Similarity of partitions:"
## cor cosineSmy obs.x obs.y
## 1 0.9999868 0.9236793 OOB Fit
## 2 0.9999867 0.9384968 OOB New
## 3 0.9999867 0.8763317 Fit New## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 184.06 secs"## Party.Democrat Party.Republican Party.NA
## NA NA 1392
## Fit 2357 2091 NA
## OOB 594 526 NA
## Party.Democrat Party.Republican Party.NA
## NA NA 1
## Fit 0.5299011 0.4700989 NA
## OOB 0.5303571 0.4696429 NA
## Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6 SKn 1920 511 638 0.43165468 0.456250000
## 2 MKy 1296 298 371 0.29136691 0.266071429
## 1 MKn 516 136 169 0.11600719 0.121428571
## 3 N 367 83 102 0.08250899 0.074107143
## 7 SKy 147 53 65 0.03304856 0.047321429
## 4 PKn 150 30 37 0.03372302 0.026785714
## 5 PKy 52 9 10 0.01169065 0.008035714
## .freqRatio.Tst
## 6 0.458333333
## 2 0.266522989
## 1 0.121408046
## 3 0.073275862
## 7 0.046695402
## 4 0.026580460
## 5 0.007183908## [1] "glbObsAll: "## [1] 6960 222## [1] "glbObsTrn: "## [1] 5568 222## [1] "glbObsFit: "## [1] 4448 221## [1] "glbObsOOB: "## [1] 1120 221## [1] "glbObsNew: "## [1] 1392 221## [1] "partition.data.training chunk: teardown: elapsed: 185.03 secs"## label step_major step_minor label_minor bgn
## 14 partition.data.training 6 0 0 369.043
## 15 select.features 7 0 0 554.170
## end elapsed
## 14 554.169 185.126
## 15 NA NAStep 7.0: select features
```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## [1] "cor(Q98059.fctr, Q98078.fctr)=0.7689"
## [1] "cor(Party.fctr, Q98059.fctr)=-0.0172"
## [1] "cor(Party.fctr, Q98078.fctr)=-0.0257"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98059.fctr as highly correlated with Q98078.fctr## [1] "cor(Q99480.fctr, Q99581.fctr)=0.7660"
## [1] "cor(Party.fctr, Q99480.fctr)=0.0344"
## [1] "cor(Party.fctr, Q99581.fctr)=0.0104"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q99581.fctr as highly correlated with Q99480.fctr## [1] "cor(Q108855.fctr, Q108856.fctr)=0.7430"
## [1] "cor(Party.fctr, Q108855.fctr)=0.0371"
## [1] "cor(Party.fctr, Q108856.fctr)=0.0140"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108856.fctr as highly correlated with Q108855.fctr## [1] "cor(Q122770.fctr, Q122771.fctr)=0.7379"
## [1] "cor(Party.fctr, Q122770.fctr)=0.0195"
## [1] "cor(Party.fctr, Q122771.fctr)=0.0348"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q122770.fctr as highly correlated with Q122771.fctr## [1] "cor(Q106272.fctr, Q106388.fctr)=0.7339"
## [1] "cor(Party.fctr, Q106272.fctr)=0.0401"
## [1] "cor(Party.fctr, Q106388.fctr)=0.0342"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q106388.fctr as highly correlated with Q106272.fctr## [1] "cor(Q100680.fctr, Q100689.fctr)=0.7292"
## [1] "cor(Party.fctr, Q100680.fctr)=-0.0158"
## [1] "cor(Party.fctr, Q100689.fctr)=-0.0257"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100680.fctr as highly correlated with Q100689.fctr## [1] "cor(Q99480.fctr, Q99716.fctr)=0.7252"
## [1] "cor(Party.fctr, Q99480.fctr)=0.0344"
## [1] "cor(Party.fctr, Q99716.fctr)=-0.0209"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q99716.fctr as highly correlated with Q99480.fctr## [1] "cor(Q120472.fctr, Q120650.fctr)=0.7126"
## [1] "cor(Party.fctr, Q120472.fctr)=0.0462"
## [1] "cor(Party.fctr, Q120650.fctr)=0.0271"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q120650.fctr as highly correlated with Q120472.fctr## [1] "cor(Q98869.fctr, Q99480.fctr)=0.7084"
## [1] "cor(Party.fctr, Q98869.fctr)=0.0277"
## [1] "cor(Party.fctr, Q99480.fctr)=0.0344"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98869.fctr as highly correlated with Q99480.fctr## [1] "cor(Q123464.fctr, Q123621.fctr)=0.7078"
## [1] "cor(Party.fctr, Q123464.fctr)=0.0136"
## [1] "cor(Party.fctr, Q123621.fctr)=0.0255"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q123464.fctr as highly correlated with Q123621.fctr## [1] "cor(Q108754.fctr, Q108855.fctr)=0.7005"
## [1] "cor(Party.fctr, Q108754.fctr)=0.0081"
## [1] "cor(Party.fctr, Q108855.fctr)=0.0371"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108754.fctr as highly correlated with Q108855.fctr## cor.y exclude.as.feat cor.y.abs cor.high.X
## Gender.fctr 0.1027400851 0 0.1027400851 <NA>
## Q115611.fctr 0.0904468203 0 0.0904468203 <NA>
## Q113181.fctr 0.0808753072 0 0.0808753072 <NA>
## Q98197.fctr 0.0549342527 0 0.0549342527 <NA>
## Q120472.fctr 0.0462030674 0 0.0462030674 <NA>
## Q116881.fctr 0.0416860293 0 0.0416860293 <NA>
## Q101596.fctr 0.0409784077 0 0.0409784077 <NA>
## Q106272.fctr 0.0400926462 0 0.0400926462 <NA>
## Q110740.fctr 0.0380691243 0 0.0380691243 <NA>
## Q108855.fctr 0.0370970211 0 0.0370970211 <NA>
## Q122771.fctr 0.0348421015 0 0.0348421015 <NA>
## Q99480.fctr 0.0344412239 0 0.0344412239 <NA>
## Q106388.fctr 0.0341579350 0 0.0341579350 Q106272.fctr
## Q115899.fctr 0.0324177950 0 0.0324177950 <NA>
## Q120014.fctr 0.0318620439 0 0.0318620439 <NA>
## Q107869.fctr 0.0304661021 0 0.0304661021 <NA>
## USER_ID 0.0302304868 1 0.0302304868 <NA>
## .pos 0.0302037138 1 0.0302037138 <NA>
## Q98869.fctr 0.0276734114 0 0.0276734114 Q99480.fctr
## Q120650.fctr 0.0270889067 0 0.0270889067 Q120472.fctr
## Q122769.fctr 0.0259739146 0 0.0259739146 <NA>
## Q123621.fctr 0.0255329743 0 0.0255329743 <NA>
## Q118117.fctr 0.0253544150 0 0.0253544150 <NA>
## Q116441.fctr 0.0237358205 0 0.0237358205 <NA>
## Q122120.fctr 0.0229287700 0 0.0229287700 <NA>
## Q119334.fctr 0.0226894034 0 0.0226894034 <NA>
## Q106993.fctr 0.0207428635 0 0.0207428635 <NA>
## Q105655.fctr 0.0198994078 0 0.0198994078 <NA>
## Q117186.fctr 0.0198853672 0 0.0198853672 <NA>
## Q122770.fctr 0.0194639697 0 0.0194639697 Q122771.fctr
## Q114152.fctr 0.0175013163 0 0.0175013163 <NA>
## Q120194.fctr 0.0172986920 0 0.0172986920 <NA>
## Q118232.fctr 0.0171321152 0 0.0171321152 <NA>
## Income.fctr 0.0159635458 0 0.0159635458 <NA>
## Q116197.fctr 0.0158561766 0 0.0158561766 <NA>
## Q102289.fctr 0.0155850393 0 0.0155850393 <NA>
## Q118233.fctr 0.0147269325 0 0.0147269325 <NA>
## Q108856.fctr 0.0140363785 0 0.0140363785 Q108855.fctr
## Q99982.fctr 0.0139727928 0 0.0139727928 <NA>
## Q117193.fctr 0.0138241599 0 0.0138241599 <NA>
## Q123464.fctr 0.0136140083 0 0.0136140083 Q123621.fctr
## Q111580.fctr 0.0132382335 0 0.0132382335 <NA>
## Q119650.fctr 0.0125645475 0 0.0125645475 <NA>
## Q118237.fctr 0.0117079669 0 0.0117079669 <NA>
## YOB 0.0116828198 1 0.0116828198 <NA>
## Q112270.fctr 0.0116157798 0 0.0116157798 <NA>
## Q116797.fctr 0.0112749656 0 0.0112749656 <NA>
## Q124742.fctr 0.0111642906 0 0.0111642906 <NA>
## Q99581.fctr 0.0103662478 0 0.0103662478 Q99480.fctr
## Q115777.fctr 0.0101315203 0 0.0101315203 <NA>
## Q101162.fctr 0.0099412952 0 0.0099412952 <NA>
## Q98578.fctr 0.0081164509 0 0.0081164509 <NA>
## Q108754.fctr 0.0080847764 0 0.0080847764 Q108855.fctr
## .rnorm 0.0078039520 0 0.0078039520 <NA>
## Q106389.fctr 0.0077498918 0 0.0077498918 <NA>
## Q96024.fctr 0.0069116541 0 0.0069116541 <NA>
## Q108343.fctr 0.0060665340 0 0.0060665340 <NA>
## Q112512.fctr 0.0056768212 0 0.0056768212 <NA>
## Q120978.fctr 0.0044187616 0 0.0044187616 <NA>
## Q106997.fctr 0.0041749086 0 0.0041749086 <NA>
## YOB.Age.dff 0.0036305828 0 0.0036305828 <NA>
## Q115610.fctr 0.0035255582 0 0.0035255582 <NA>
## Q116953.fctr 0.0029786716 0 0.0029786716 <NA>
## Q115602.fctr 0.0027844465 0 0.0027844465 <NA>
## Q100010.fctr 0.0024291540 0 0.0024291540 <NA>
## Q108617.fctr 0.0024119725 0 0.0024119725 <NA>
## Q100562.fctr 0.0017132769 0 0.0017132769 <NA>
## Q107491.fctr 0.0014031814 0 0.0014031814 <NA>
## Q114748.fctr 0.0008477228 0 0.0008477228 <NA>
## Q112478.fctr -0.0001517248 0 0.0001517248 <NA>
## Q103293.fctr -0.0005915534 0 0.0005915534 <NA>
## Q102674.fctr -0.0009759844 0 0.0009759844 <NA>
## Q108950.fctr -0.0010567028 0 0.0010567028 <NA>
## Q113584.fctr -0.0011387024 0 0.0011387024 <NA>
## Q102906.fctr -0.0011540297 0 0.0011540297 <NA>
## Q104996.fctr -0.0012202806 0 0.0012202806 <NA>
## Q116601.fctr -0.0022379241 0 0.0022379241 <NA>
## Q116448.fctr -0.0031731051 0 0.0031731051 <NA>
## Q106042.fctr -0.0032327194 0 0.0032327194 <NA>
## Q121011.fctr -0.0037329030 0 0.0037329030 <NA>
## Q113992.fctr -0.0041479796 0 0.0041479796 <NA>
## Q111220.fctr -0.0055758571 0 0.0055758571 <NA>
## Q124122.fctr -0.0061257448 0 0.0061257448 <NA>
## Q121700.fctr -0.0067756198 0 0.0067756198 <NA>
## Q114961.fctr -0.0079206587 0 0.0079206587 <NA>
## Q109367.fctr -0.0080456026 0 0.0080456026 <NA>
## Q120012.fctr -0.0084652930 0 0.0084652930 <NA>
## Q114517.fctr -0.0084741753 0 0.0084741753 <NA>
## Q119851.fctr -0.0093381833 0 0.0093381833 <NA>
## Q115390.fctr -0.0119300319 0 0.0119300319 <NA>
## Q102687.fctr -0.0120079165 0 0.0120079165 <NA>
## Q118892.fctr -0.0125250379 0 0.0125250379 <NA>
## YOB.Age.fctr -0.0129198495 0 0.0129198495 <NA>
## Q111848.fctr -0.0141099384 0 0.0141099384 <NA>
## Q108342.fctr -0.0151842510 0 0.0151842510 <NA>
## Q100680.fctr -0.0157762454 0 0.0157762454 Q100689.fctr
## Q114386.fctr -0.0168013326 0 0.0168013326 <NA>
## Q98059.fctr -0.0171637755 0 0.0171637755 Q98078.fctr
## Q102089.fctr -0.0174087944 0 0.0174087944 <NA>
## Q115195.fctr -0.0174522586 0 0.0174522586 <NA>
## Q113583.fctr -0.0191894717 0 0.0191894717 <NA>
## Q105840.fctr -0.0195569165 0 0.0195569165 <NA>
## Q121699.fctr -0.0196933075 0 0.0196933075 <NA>
## Q120379.fctr -0.0206291292 0 0.0206291292 <NA>
## Q99716.fctr -0.0209286674 0 0.0209286674 Q99480.fctr
## Q98078.fctr -0.0256516490 0 0.0256516490 <NA>
## Q100689.fctr -0.0256915080 0 0.0256915080 <NA>
## Q101163.fctr -0.0295046473 0 0.0295046473 <NA>
## Edn.fctr -0.0359295351 0 0.0359295351 <NA>
## Hhold.fctr -0.0511386673 0 0.0511386673 <NA>
## .clusterid -0.1012091920 1 0.1012091920 <NA>
## .clusterid.fctr -0.1012091920 0 0.1012091920 <NA>
## Q109244.fctr -0.1203812469 0 0.1203812469 <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## Gender.fctr 1.561033 0.05387931 FALSE FALSE FALSE
## Q115611.fctr 1.194859 0.05387931 FALSE FALSE FALSE
## Q113181.fctr 1.006354 0.05387931 FALSE FALSE FALSE
## Q98197.fctr 1.129371 0.05387931 FALSE FALSE FALSE
## Q120472.fctr 1.292633 0.05387931 FALSE FALSE FALSE
## Q116881.fctr 1.010066 0.05387931 FALSE FALSE FALSE
## Q101596.fctr 1.041667 0.05387931 FALSE FALSE FALSE
## Q106272.fctr 1.116536 0.05387931 FALSE FALSE FALSE
## Q110740.fctr 1.050779 0.05387931 FALSE FALSE FALSE
## Q108855.fctr 1.273980 0.05387931 FALSE FALSE FALSE
## Q122771.fctr 1.414753 0.05387931 FALSE FALSE FALSE
## Q99480.fctr 1.225404 0.05387931 FALSE FALSE FALSE
## Q106388.fctr 1.065033 0.05387931 FALSE FALSE FALSE
## Q115899.fctr 1.197849 0.05387931 FALSE FALSE FALSE
## Q120014.fctr 1.044944 0.05387931 FALSE FALSE FALSE
## Q107869.fctr 1.211050 0.05387931 FALSE FALSE FALSE
## USER_ID 1.000000 100.00000000 FALSE FALSE FALSE
## .pos 1.000000 100.00000000 FALSE FALSE FALSE
## Q98869.fctr 1.080860 0.05387931 FALSE FALSE FALSE
## Q120650.fctr 1.896247 0.05387931 FALSE FALSE FALSE
## Q122769.fctr 1.060606 0.05387931 FALSE FALSE FALSE
## Q123621.fctr 1.466381 0.05387931 FALSE FALSE FALSE
## Q118117.fctr 1.174006 0.05387931 FALSE FALSE FALSE
## Q116441.fctr 1.019645 0.05387931 FALSE FALSE FALSE
## Q122120.fctr 1.297443 0.05387931 FALSE FALSE FALSE
## Q119334.fctr 1.081498 0.05387931 FALSE FALSE FALSE
## Q106993.fctr 1.327392 0.05387931 FALSE FALSE FALSE
## Q105655.fctr 1.079316 0.05387931 FALSE FALSE FALSE
## Q117186.fctr 1.053878 0.05387931 FALSE FALSE FALSE
## Q122770.fctr 1.008802 0.05387931 FALSE FALSE FALSE
## Q114152.fctr 1.027617 0.05387931 FALSE FALSE FALSE
## Q120194.fctr 1.016716 0.05387931 FALSE FALSE FALSE
## Q118232.fctr 1.365812 0.05387931 FALSE FALSE FALSE
## Income.fctr 1.256724 0.12571839 FALSE FALSE FALSE
## Q116197.fctr 1.073778 0.05387931 FALSE FALSE FALSE
## Q102289.fctr 1.033482 0.05387931 FALSE FALSE FALSE
## Q118233.fctr 1.199142 0.05387931 FALSE FALSE FALSE
## Q108856.fctr 1.080645 0.05387931 FALSE FALSE FALSE
## Q99982.fctr 1.339380 0.05387931 FALSE FALSE FALSE
## Q117193.fctr 1.140665 0.05387931 FALSE FALSE FALSE
## Q123464.fctr 1.326681 0.05387931 FALSE FALSE FALSE
## Q111580.fctr 1.024977 0.05387931 FALSE FALSE FALSE
## Q119650.fctr 1.456978 0.05387931 FALSE FALSE FALSE
## Q118237.fctr 1.088017 0.05387931 FALSE FALSE FALSE
## YOB 1.027559 1.41882184 FALSE FALSE FALSE
## Q112270.fctr 1.254284 0.05387931 FALSE FALSE FALSE
## Q116797.fctr 1.009589 0.05387931 FALSE FALSE FALSE
## Q124742.fctr 2.565379 0.05387931 FALSE FALSE FALSE
## Q99581.fctr 1.375000 0.05387931 FALSE FALSE FALSE
## Q115777.fctr 1.140288 0.05387931 FALSE FALSE FALSE
## Q101162.fctr 1.103229 0.05387931 FALSE FALSE FALSE
## Q98578.fctr 1.093556 0.05387931 FALSE FALSE FALSE
## Q108754.fctr 1.008090 0.05387931 FALSE FALSE FALSE
## .rnorm 1.000000 100.00000000 FALSE FALSE FALSE
## Q106389.fctr 1.341307 0.05387931 FALSE FALSE TRUE
## Q96024.fctr 1.144428 0.05387931 FALSE FALSE TRUE
## Q108343.fctr 1.064910 0.05387931 FALSE FALSE TRUE
## Q112512.fctr 1.299253 0.05387931 FALSE FALSE TRUE
## Q120978.fctr 1.131963 0.05387931 FALSE FALSE TRUE
## Q106997.fctr 1.177632 0.05387931 FALSE FALSE TRUE
## YOB.Age.dff 1.007778 0.35919540 FALSE FALSE TRUE
## Q115610.fctr 1.359695 0.05387931 FALSE FALSE TRUE
## Q116953.fctr 1.039180 0.05387931 FALSE FALSE TRUE
## Q115602.fctr 1.322302 0.05387931 FALSE FALSE TRUE
## Q100010.fctr 1.268156 0.05387931 FALSE FALSE TRUE
## Q108617.fctr 1.390618 0.05387931 FALSE FALSE TRUE
## Q100562.fctr 1.217215 0.05387931 FALSE FALSE TRUE
## Q107491.fctr 1.419021 0.05387931 FALSE FALSE TRUE
## Q114748.fctr 1.051125 0.05387931 FALSE FALSE TRUE
## Q112478.fctr 1.113648 0.05387931 FALSE FALSE TRUE
## Q103293.fctr 1.122287 0.05387931 FALSE FALSE TRUE
## Q102674.fctr 1.073412 0.05387931 FALSE FALSE TRUE
## Q108950.fctr 1.103872 0.05387931 FALSE FALSE TRUE
## Q113584.fctr 1.212486 0.05387931 FALSE FALSE TRUE
## Q102906.fctr 1.053396 0.05387931 FALSE FALSE TRUE
## Q104996.fctr 1.173840 0.05387931 FALSE FALSE TRUE
## Q116601.fctr 1.394914 0.05387931 FALSE FALSE TRUE
## Q116448.fctr 1.161031 0.05387931 FALSE FALSE TRUE
## Q106042.fctr 1.247738 0.05387931 FALSE FALSE TRUE
## Q121011.fctr 1.153676 0.05387931 FALSE FALSE TRUE
## Q113992.fctr 1.267442 0.05387931 FALSE FALSE TRUE
## Q111220.fctr 1.262849 0.05387931 FALSE FALSE TRUE
## Q124122.fctr 1.412807 0.05387931 FALSE FALSE TRUE
## Q121700.fctr 1.708221 0.05387931 FALSE FALSE TRUE
## Q114961.fctr 1.250436 0.05387931 FALSE FALSE FALSE
## Q109367.fctr 1.008571 0.05387931 FALSE FALSE FALSE
## Q120012.fctr 1.047185 0.05387931 FALSE FALSE FALSE
## Q114517.fctr 1.183374 0.05387931 FALSE FALSE FALSE
## Q119851.fctr 1.244519 0.05387931 FALSE FALSE FALSE
## Q115390.fctr 1.150505 0.05387931 FALSE FALSE FALSE
## Q102687.fctr 1.256545 0.05387931 FALSE FALSE FALSE
## Q118892.fctr 1.347380 0.05387931 FALSE FALSE FALSE
## YOB.Age.fctr 1.005794 0.16163793 FALSE FALSE FALSE
## Q111848.fctr 1.113602 0.05387931 FALSE FALSE FALSE
## Q108342.fctr 1.048292 0.05387931 FALSE FALSE FALSE
## Q100680.fctr 1.102386 0.05387931 FALSE FALSE FALSE
## Q114386.fctr 1.092072 0.05387931 FALSE FALSE FALSE
## Q98059.fctr 1.493810 0.05387931 FALSE FALSE FALSE
## Q102089.fctr 1.055963 0.05387931 FALSE FALSE FALSE
## Q115195.fctr 1.065496 0.05387931 FALSE FALSE FALSE
## Q113583.fctr 1.102515 0.05387931 FALSE FALSE FALSE
## Q105840.fctr 1.275362 0.05387931 FALSE FALSE FALSE
## Q121699.fctr 1.507127 0.05387931 FALSE FALSE FALSE
## Q120379.fctr 1.046326 0.05387931 FALSE FALSE FALSE
## Q99716.fctr 1.328693 0.05387931 FALSE FALSE FALSE
## Q98078.fctr 1.266595 0.05387931 FALSE FALSE FALSE
## Q100689.fctr 1.029800 0.05387931 FALSE FALSE FALSE
## Q101163.fctr 1.327394 0.05387931 FALSE FALSE FALSE
## Edn.fctr 1.392610 0.14367816 FALSE FALSE FALSE
## Hhold.fctr 1.525094 0.12571839 FALSE FALSE FALSE
## .clusterid 1.784041 0.07183908 FALSE FALSE FALSE
## .clusterid.fctr 1.784041 0.07183908 FALSE FALSE FALSE
## Q109244.fctr 1.125916 0.05387931 FALSE FALSE FALSE## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## [1] cor.y exclude.as.feat cor.y.abs cor.high.X
## [5] freqRatio percentUnique zeroVar nzv
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## YOB.Age.dff
## 438
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024 .lcn
## 2836 2858 1392## [1] "glb_feats_df:"## [1] 113 12## id exclude.as.feat rsp_var
## Party.fctr Party.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## USER_ID USER_ID 0.03023049 TRUE 0.03023049 <NA>
## Party.fctr Party.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## USER_ID 1 100 FALSE FALSE FALSE
## Party.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID <NA> NA FALSE TRUE
## Party.fctr <NA> NA NA NA
## rsp_var
## USER_ID NA
## Party.fctr TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end
## 15 select.features 7 0 0 554.170 560.871
## 16 fit.models 8 0 0 560.872 NA
## elapsed
## 15 6.701
## 16 NAStep 8.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 561.605 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
glbgetModelSelectFormula <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
glbgetDisplayModelsDf <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#glbgetDisplayModelsDf()
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 561.605 561.641
## 2 fit.models_0_MFO 1 1 myMFO_classfr 561.641 NA
## elapsed
## 1 0.036
## 2 NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.411000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] D R
## Levels: D R
## [1] "unique.prob:"
## y
## D R
## 0.5299011 0.4700989
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.767000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used## [1] "myfit_mdl: train diagnostics complete: 0.770000 secs"## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989
## Prediction
## Reference D R
## D 2357 0
## R 2091 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5299011 0.0000000 0.5151055 0.5446573 0.5299011
## AccuracyPValue McnemarPValue
## 0.5061116 0.0000000
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989## Prediction
## Reference D R
## D 594 0
## R 526 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.303571e-01 0.000000e+00 5.006349e-01 5.599195e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 5.121815e-01 5.700089e-116
## [1] "myfit_mdl: predict complete: 7.703000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.345
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.005 0.5 1 0
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.5299011
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5151055 0.5446573 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 1 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5303571
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5006349 0.5599195 0
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989
## [1] "myfit_mdl: exit: 7.794000 secs"
## label step_major step_minor label_minor bgn
## 2 fit.models_0_MFO 1 1 myMFO_classfr 561.641
## 3 fit.models_0_Random 1 2 myrandom_classfr 569.442
## end elapsed
## 2 569.442 7.801
## 3 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.408000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.806000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used
## [1] "myfit_mdl: train diagnostics complete: 0.808000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 2357 0
## R 2091 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5299011 0.0000000 0.5151055 0.5446573 0.5299011
## AccuracyPValue McnemarPValue
## 0.5061116 0.0000000
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 594 0
## R 526 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.303571e-01 0.000000e+00 5.006349e-01 5.599195e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 5.121815e-01 5.700089e-116
## [1] "myfit_mdl: predict complete: 9.589000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.39 0.002 0.4919921
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5243954 0.4595887 0.4904337 0.55
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.5299011 0.5151055
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5446573 0 0.484138 0.510101
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.4581749 0.5167262 0.55 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5303571 0.5006349 0.5599195
## max.Kappa.OOB
## 1 0
## [1] "in Random.Classifier$prob"
## [1] "myfit_mdl: exit: 11.628000 secs"# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 569.442 581.083 11.642
## 4 581.084 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.671000 secs"## Loading required package: glmnet## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expand## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.0024 on full training set
## [1] "myfit_mdl: train complete: 1.444000 secs"
## alpha lambda
## 1 0.1 0.00240379
## Length Class Mode
## a0 58 -none- numeric
## beta 232 dgCMatrix S4
## df 58 -none- numeric
## dim 2 -none- numeric
## lambda 58 -none- numeric
## dev.ratio 58 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 4 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Q109244.fctrNo
## -0.23744521 -0.03825231 0.18968545 0.41608608
## Q109244.fctrYes
## -1.17431968
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "Gender.fctrF" "Gender.fctrM" "Q109244.fctrNo"
## [5] "Q109244.fctrYes"
## [1] "myfit_mdl: train diagnostics complete: 1.544000 secs"
## Prediction
## Reference D R
## D 1026 1331
## R 448 1643
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.000450e-01 2.159029e-01 5.854740e-01 6.144844e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 2.736485e-21 4.227415e-97
## Prediction
## Reference D R
## D 383 211
## R 228 298
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.080357e-01 2.117127e-01 5.787451e-01 6.367572e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.703527e-08 4.450828e-01
## [1] "myfit_mdl: predict complete: 8.798000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.764 0.059 0.5930969
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.6491303 0.5370636 0.6377431 0.45
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.648766 0.600045 0.585474
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.6144844 0.2159029 0.6056605 0.6447811
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.5665399 0.6504766 0.5 0.5758454
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.6080357 0.5787451 0.6367572
## max.Kappa.OOB
## 1 0.2117127
## [1] "myfit_mdl: exit: 8.908000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.682000 secs"## Loading required package: rpart## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0411 on full training set
## [1] "myfit_mdl: train complete: 2.207000 secs"## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4448
##
## CP nsplit rel error
## 1 0.08225729 0 1.0000000
## 2 0.05930177 1 0.9177427
## 3 0.04112865 2 0.8584409
##
## Variable importance
## Q109244.fctrYes Q109244.fctrNo Gender.fctrM Gender.fctrF
## 83 15 1 1
##
## Node number 1: 4448 observations, complexity param=0.08225729
## predicted class=D expected loss=0.4700989 P(node) =1
## class counts: 2357 2091
## probabilities: 0.530 0.470
## left son=2 (724 obs) right son=3 (3724 obs)
## Primary splits:
## Q109244.fctrYes < 0.5 to the right, improve=128.50730, (0 missing)
## Q109244.fctrNo < 0.5 to the left, improve= 77.73133, (0 missing)
## Gender.fctrM < 0.5 to the left, improve= 26.03060, (0 missing)
## Gender.fctrF < 0.5 to the right, improve= 25.54702, (0 missing)
##
## Node number 2: 724 observations
## predicted class=D expected loss=0.1975138 P(node) =0.1627698
## class counts: 581 143
## probabilities: 0.802 0.198
##
## Node number 3: 3724 observations, complexity param=0.05930177
## predicted class=R expected loss=0.4769066 P(node) =0.8372302
## class counts: 1776 1948
## probabilities: 0.477 0.523
## left son=6 (1774 obs) right son=7 (1950 obs)
## Primary splits:
## Q109244.fctrNo < 0.5 to the left, improve=22.827280, (0 missing)
## Gender.fctrM < 0.5 to the left, improve= 9.571336, (0 missing)
## Gender.fctrF < 0.5 to the right, improve= 8.385185, (0 missing)
## Surrogate splits:
## Gender.fctrM < 0.5 to the left, agree=0.564, adj=0.085, (0 split)
## Gender.fctrF < 0.5 to the right, agree=0.556, adj=0.069, (0 split)
##
## Node number 6: 1774 observations
## predicted class=D expected loss=0.4650507 P(node) =0.3988309
## class counts: 949 825
## probabilities: 0.535 0.465
##
## Node number 7: 1950 observations
## predicted class=R expected loss=0.4241026 P(node) =0.4383993
## class counts: 827 1123
## probabilities: 0.424 0.576
##
## n= 4448
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4448 2091 D (0.5299011 0.4700989)
## 2) Q109244.fctrYes>=0.5 724 143 D (0.8024862 0.1975138) *
## 3) Q109244.fctrYes< 0.5 3724 1776 R (0.4769066 0.5230934)
## 6) Q109244.fctrNo< 0.5 1774 825 D (0.5349493 0.4650507) *
## 7) Q109244.fctrNo>=0.5 1950 827 R (0.4241026 0.5758974) *
## [1] "myfit_mdl: train diagnostics complete: 2.997000 secs"
## Prediction
## Reference D R
## D 1530 827
## R 968 1123
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.964478e-01 1.869050e-01 5.818581e-01 6.109108e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 2.460219e-19 9.517167e-04
## Prediction
## Reference D R
## D 383 211
## R 228 298
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.080357e-01 2.117127e-01 5.787451e-01 6.367572e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.703527e-08 4.450828e-01
## [1] "myfit_mdl: predict complete: 10.321000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.517 0.02 0.5930969
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.6491303 0.5370636 0.6279573 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.555803 0.5964458 0.5818581
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.6109108 0.1868977 0.6056605 0.6447811
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.5665399 0.6398875 0.5 0.5758454
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.6080357 0.5787451 0.6367572
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.2117127 0.01075781 0.02175241
## [1] "myfit_mdl: exit: 10.430000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## bgn end elapsed
## 4 581.084 600.463 19.379
## 5 600.464 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indepVar: Q109244.fctr,Gender.fctr,Q109244.fctr:Q106272.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q98078.fctr"
## [1] "myfit_mdl: setup complete: 0.706000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0112 on full training set
## [1] "myfit_mdl: train complete: 5.344000 secs"
## Length Class Mode
## a0 72 -none- numeric
## beta 3744 dgCMatrix S4
## df 72 -none- numeric
## dim 2 -none- numeric
## lambda 72 -none- numeric
## dev.ratio 72 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 52 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF
## -0.2570543810 -0.0178583107
## Gender.fctrM Q109244.fctrNo
## 0.1383521771 0.1854900367
## Q109244.fctrYes Q109244.fctrNA:Q100689.fctrNo
## -0.8125344796 -0.0221702652
## Q109244.fctrNA:Q100689.fctrYes Q109244.fctrNo:Q100689.fctrYes
## -0.2426488188 -0.0250405661
## Q109244.fctrYes:Q100689.fctrYes Q109244.fctrNA:Q106272.fctrYes
## -0.1053473127 0.1794429578
## Q109244.fctrNo:Q106272.fctrYes Q109244.fctrYes:Q106272.fctrYes
## 0.2327110322 -0.1527846472
## Q109244.fctrNA:Q108855.fctrYes! Q109244.fctrNo:Q108855.fctrYes!
## 0.1022982699 0.2152280710
## Q109244.fctrYes:Q108855.fctrYes! Q109244.fctrNo:Q120472.fctrArt
## -0.0008042052 -0.0350614839
## Q109244.fctrNA:Q120472.fctrScience Q109244.fctrNo:Q120472.fctrScience
## 0.1304557686 0.0540335220
## Q109244.fctrNA:Q122771.fctrPc Q109244.fctrYes:Q122771.fctrPc
## -0.0124450523 -0.1426182994
## Q109244.fctrNA:Q122771.fctrPt Q109244.fctrNo:Q122771.fctrPt
## 0.2177627629 0.0699180277
## Q109244.fctrYes:Q123621.fctrNo Q109244.fctrNo:Q123621.fctrYes
## -0.0416556443 0.1249272393
## Q109244.fctrNA:Q98078.fctrNo Q109244.fctrNo:Q98078.fctrNo
## -0.2222970510 -0.0046675791
## Q109244.fctrNA:Q98078.fctrYes Q109244.fctrYes:Q98078.fctrYes
## -0.1628825704 -0.1241586392
## Q109244.fctrNA:Q99480.fctrNo Q109244.fctrNo:Q99480.fctrNo
## -0.3711403917 -0.3640717183
## Q109244.fctrNA:Q99480.fctrYes
## 0.2728995842
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrF
## -0.2600960490 -0.0150843813
## Gender.fctrM Q109244.fctrNo
## 0.1421784149 0.1945914562
## Q109244.fctrYes Q109244.fctrNA:Q100689.fctrNo
## -0.8196847017 -0.0369069597
## Q109244.fctrNA:Q100689.fctrYes Q109244.fctrNo:Q100689.fctrYes
## -0.2591146648 -0.0342030699
## Q109244.fctrYes:Q100689.fctrYes Q109244.fctrNA:Q106272.fctrYes
## -0.1053446864 0.1903201428
## Q109244.fctrNo:Q106272.fctrYes Q109244.fctrYes:Q106272.fctrYes
## 0.2358476224 -0.1535966865
## Q109244.fctrNA:Q108855.fctrYes! Q109244.fctrNo:Q108855.fctrYes!
## 0.1137934751 0.2181434018
## Q109244.fctrYes:Q108855.fctrYes! Q109244.fctrNo:Q120472.fctrArt
## -0.0001127586 -0.0453696146
## Q109244.fctrNA:Q120472.fctrScience Q109244.fctrNo:Q120472.fctrScience
## 0.1365991768 0.0518527654
## Q109244.fctrNA:Q122771.fctrPc Q109244.fctrYes:Q122771.fctrPc
## -0.0212634504 -0.1405559544
## Q109244.fctrNA:Q122771.fctrPt Q109244.fctrNo:Q122771.fctrPt
## 0.2217650111 0.0740047810
## Q109244.fctrNA:Q123621.fctrNo Q109244.fctrYes:Q123621.fctrNo
## 0.0062675191 -0.0438657012
## Q109244.fctrNo:Q123621.fctrYes Q109244.fctrNA:Q98078.fctrNo
## 0.1296692666 -0.2367964661
## Q109244.fctrNo:Q98078.fctrNo Q109244.fctrNA:Q98078.fctrYes
## -0.0129763601 -0.1782031098
## Q109244.fctrYes:Q98078.fctrYes Q109244.fctrNA:Q99480.fctrNo
## -0.1264603087 -0.3680585880
## Q109244.fctrNo:Q99480.fctrNo Q109244.fctrNA:Q99480.fctrYes
## -0.3716336729 0.2920912749
## [1] "myfit_mdl: train diagnostics complete: 6.012000 secs"
## Prediction
## Reference D R
## D 1510 847
## R 838 1253
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.211781e-01 2.398215e-01 6.067340e-01 6.354628e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 7.303873e-35 8.454789e-01
## Prediction
## Reference D R
## D 462 132
## R 323 203
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.937500e-01 1.671842e-01 5.643251e-01 6.226811e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 1.134527e-05 5.227279e-19
## [1] "myfit_mdl: predict complete: 14.393000 secs"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats
## 1 Q109244.fctr,Gender.fctr,Q109244.fctr:Q106272.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q98078.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 4.618 0.325
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6199399 0.6406449 0.5992348 0.6671877
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.597948 0.6075379
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.606734 0.6354628 0.2118159
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5890144 0.6228956 0.5551331 0.6473848
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.55 0.4715447 0.59375
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5643251 0.6226811 0.1671842
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01277045 0.02587927
## [1] "myfit_mdl: exit: 14.536000 secs"# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## 6 fit.models_0_Low.cor.X 1 5 glmnet
## bgn end elapsed
## 5 600.464 615.026 14.562
## 6 615.026 NA NAindepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q101596.fctr,Q110740.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.678000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0518 on full training set
## [1] "myfit_mdl: train complete: 17.878000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 81 -none- numeric
## beta 19926 dgCMatrix S4
## df 81 -none- numeric
## dim 2 -none- numeric
## lambda 81 -none- numeric
## dev.ratio 81 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 246 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Hhold.fctrMKy
## -0.199400716 -0.013490459 0.074935942 0.067730495
## Hhold.fctrPKn Q101163.fctrDad Q101163.fctrMom Q106042.fctrNo
## -0.357041632 0.028232813 -0.053078477 0.037628297
## Q106389.fctrNo Q109244.fctrNo Q109244.fctrYes Q110740.fctrPC
## 0.032656049 0.276320776 -0.797684420 0.028338782
## Q113181.fctrNo Q113181.fctrYes Q115611.fctrNo Q115611.fctrYes
## -0.066965675 0.163526018 -0.098625750 0.253516235
## Q116881.fctrRight Q119851.fctrNo Q120379.fctrNo Q121699.fctrNo
## 0.103973480 0.051105480 0.006078645 0.024215343
## Q121699.fctrYes Q121700.fctrYes Q98197.fctrNo Q98197.fctrYes
## -0.010725525 -0.008279491 -0.186697269 0.014819196
## Q98869.fctrNo
## -0.113179212
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrF
## -0.203786081 -0.012793232
## Gender.fctrM Hhold.fctrMKy
## 0.081245869 0.073998539
## Hhold.fctrPKn Q101163.fctrDad
## -0.386345229 0.032735974
## Q101163.fctrMom Q106042.fctrNo
## -0.063109692 0.050132577
## Q106389.fctrNo Q109244.fctrNo
## 0.044408373 0.281269524
## Q109244.fctrYes Q110740.fctrPC
## -0.815401804 0.039091491
## Q113181.fctrNo Q113181.fctrYes
## -0.074727875 0.164041363
## Q115390.fctrYes Q115611.fctrNo
## -0.001034204 -0.106715216
## Q115611.fctrYes Q116881.fctrRight
## 0.257207423 0.120554506
## Q118232.fctrId Q119851.fctrNo
## -0.004093904 0.063852075
## Q120194.fctrStudy first Q120379.fctrNo
## -0.010955779 0.017692409
## Q121699.fctrNo Q121699.fctrYes
## 0.028993804 -0.021764364
## Q121700.fctrYes Q98197.fctrNo
## -0.022134399 -0.197953310
## Q98197.fctrYes Q98869.fctrNo
## 0.012414927 -0.125871646
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.002544715
## [1] "myfit_mdl: train diagnostics complete: 18.541000 secs"
## Prediction
## Reference D R
## D 1744 613
## R 988 1103
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.400629e-01 2.701563e-01 6.257566e-01 6.541851e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 4.501285e-50 9.010123e-21
## Prediction
## Reference D R
## D 445 149
## R 261 265
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.339286e-01 2.560816e-01 6.049471e-01 6.622043e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 1.595902e-12 4.207838e-08
## [1] "myfit_mdl: predict complete: 32.718000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q101596.fctr,Q110740.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 17.089 1.887
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6337112 0.7399236 0.5274988 0.6972623
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5794589 0.6349657
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6257566 0.6541851 0.2603136
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6264803 0.7491582 0.5038023 0.685118
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.5638298 0.6339286
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.6049471 0.6622043 0.2560816
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01072305 0.02260482
## [1] "myfit_mdl: exit: 33.132000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 6 fit.models_0_Low.cor.X 1 5 glmnet 615.026 648.216
## 7 fit.models_0_end 1 6 teardown 648.216 NA
## elapsed
## 6 33.19
## 7 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 16 fit.models 8 0 0 560.872 648.23 87.359
## 17 fit.models 8 1 1 648.231 NA NA```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 653.178 NA NA## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 653.178 653.191
## 2 fit.models_1_All.X 1 1 setup 653.192 NA
## elapsed
## 1 0.013
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 653.192 653.2
## 3 fit.models_1_All.X 1 2 glmnet 653.200 NA
## elapsed
## 2 0.008
## 3 NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.690000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0518 on full training set
## [1] "myfit_mdl: train complete: 20.950000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 80 -none- numeric
## beta 20960 dgCMatrix S4
## df 80 -none- numeric
## dim 2 -none- numeric
## lambda 80 -none- numeric
## dev.ratio 80 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 262 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM
## -0.198712405 -0.011825344 0.074355147
## Hhold.fctrMKy Hhold.fctrPKn Q101163.fctrDad
## 0.065913846 -0.359446982 0.029943415
## Q101163.fctrMom Q106042.fctrNo Q106389.fctrNo
## -0.049503612 0.039110805 0.032041904
## Q109244.fctrNo Q109244.fctrYes Q110740.fctrPC
## 0.277893187 -0.793733392 0.026392170
## Q113181.fctrNo Q113181.fctrYes Q115611.fctrNo
## -0.063965281 0.163829736 -0.094693744
## Q115611.fctrYes Q116881.fctrRight Q119851.fctrNo
## 0.249965058 0.102858246 0.050896551
## Q120379.fctrNo Q120472.fctrScience Q121699.fctrNo
## 0.003510917 0.008850509 0.025110000
## Q121699.fctrYes Q121700.fctrYes Q98197.fctrNo
## -0.012775044 -0.005214162 -0.178058529
## Q98197.fctrYes Q98869.fctrNo Q99480.fctrNo
## 0.019844641 -0.108372331 -0.089556160
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrF
## -0.203560439 -0.010364545
## Gender.fctrM Hhold.fctrMKy
## 0.080196690 0.072060211
## Hhold.fctrPKn Q101163.fctrDad
## -0.388896571 0.034363706
## Q101163.fctrMom Q106042.fctrNo
## -0.059043367 0.051735068
## Q106389.fctrNo Q109244.fctrNo
## 0.043314178 0.282819915
## Q109244.fctrYes Q110740.fctrPC
## -0.810926976 0.036622469
## Q113181.fctrNo Q113181.fctrYes
## -0.071382987 0.164577272
## Q115390.fctrYes Q115611.fctrNo
## -0.003118841 -0.101630152
## Q115611.fctrYes Q116881.fctrRight
## 0.253357174 0.118922275
## Q118232.fctrId Q119851.fctrNo
## -0.003928064 0.062759027
## Q120194.fctrStudy first Q120379.fctrNo
## -0.012009198 0.014063253
## Q120472.fctrScience Q121699.fctrNo
## 0.018955576 0.027742541
## Q121699.fctrYes Q121700.fctrYes
## -0.025797723 -0.018900284
## Q98197.fctrNo Q98197.fctrYes
## -0.188181477 0.018225390
## Q98869.fctrNo Q99480.fctrNo
## -0.120949492 -0.101614944
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.003478385
## [1] "myfit_mdl: train diagnostics complete: 21.584000 secs"
## Prediction
## Reference D R
## D 1750 607
## R 990 1101
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.409622e-01 2.718210e-01 6.266631e-01 6.550761e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 7.166838e-51 1.189393e-21
## Prediction
## Reference D R
## D 444 150
## R 258 268
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.357143e-01 2.600365e-01 6.067573e-01 6.639561e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 6.649800e-13 1.175344e-07
## [1] "myfit_mdl: predict complete: 36.056000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 20.142 2.011
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6345058 0.7424692 0.5265423 0.6986118
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5796262 0.6348906
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6266631 0.6550761 0.2603303
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6284902 0.7474747 0.5095057 0.6840842
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.5677966 0.6357143
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.6067573 0.6639561 0.2600365
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01087901 0.02284947
## [1] "myfit_mdl: exit: 36.493000 secs"## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 653.200 689.735
## 4 fit.models_1_preProc 1 3 preProc 689.736 NA
## elapsed
## 3 36.535
## 4 NA## min.elapsedtime.everything
## MFO###myMFO_classfr 0.345
## Random###myrandom_classfr 0.390
## Max.cor.Y.rcv.1X1###glmnet 0.764
## Max.cor.Y##rcv#rpart 1.517
## Interact.High.cor.Y##rcv#glmnet 4.618
## Low.cor.X##rcv#glmnet 17.089
## All.X##rcv#glmnet 20.142## label step_major step_minor label_minor bgn end
## 4 fit.models_1_preProc 1 3 preProc 689.736 690.21
## 5 fit.models_1_end 1 4 teardown 690.211 NA
## elapsed
## 4 0.474
## 5 NA## label step_major step_minor label_minor bgn end elapsed
## 17 fit.models 8 1 1 648.231 690.22 41.989
## 18 fit.models 8 2 2 690.221 NA NA```{r fit.models_2, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)Step 8.2: fit models
```{r fit.data.training_0, cache=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification)
# mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
# mdlEnsembleComps <- gsub(paste0("^",
# gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
# "", mdlEnsembleComps)
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$prob %in% mdlEnsembleComps)] else
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlEnsembleComps)]
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
# glb_fin_mdl uses the same coefficients as glb_sel_mdl,
# so copy the "Final" columns into "non-Final" columns
glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))
print(setdiff(names(glbObsOOB), names(glbObsAll)))
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)Step 8.2: fit models
Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 14 partition.data.training 6 0 0 369.043
## 2 inspect.data 2 0 0 16.548
## 13 cluster.data 5 0 0 214.568
## 16 fit.models 8 0 0 560.872
## 17 fit.models 8 1 1 648.231
## 3 scrub.data 2 1 1 174.407
## 1 import.data 1 0 0 6.392
## 15 select.features 7 0 0 554.170
## 11 extract.features.end 3 6 6 212.762
## 12 manage.missing.data 4 0 0 213.680
## 10 extract.features.string 3 5 5 212.673
## 9 extract.features.text 3 4 4 212.613
## 7 extract.features.image 3 2 2 212.517
## 4 transform.data 2 2 2 212.405
## 6 extract.features.datetime 3 1 1 212.474
## 8 extract.features.price 3 3 3 212.574
## 5 extract.features 3 0 0 212.451
## end elapsed duration
## 14 554.169 185.126 185.126
## 2 174.407 157.859 157.859
## 13 369.043 154.475 154.475
## 16 648.230 87.359 87.358
## 17 690.220 41.989 41.989
## 3 212.405 37.998 37.998
## 1 16.547 10.155 10.155
## 15 560.871 6.701 6.701
## 11 213.679 0.917 0.917
## 12 214.567 0.887 0.887
## 10 212.762 0.089 0.089
## 9 212.673 0.060 0.060
## 7 212.574 0.057 0.057
## 4 212.451 0.046 0.046
## 6 212.517 0.043 0.043
## 8 212.612 0.038 0.038
## 5 212.473 0.022 0.022
## [1] "Total Elapsed Time: 690.22 secs"